text:
. To alleviate this, we suggest to apply clustering
               to the localization region of interest first and then use kernalbased
               fingerprinting localization for each cluster. A byproduct of
               clustering is that the computational load for each cluster is also
               significantly reduced. To further reduce the computational load
               within each cluster, we also suggest to apply principal component
               compression to the raw RSS measurements to reduce their
               dimensionality. The rationale for applying principal component
               compression is that the distributions of the RSS measurements
               at all calibration points (CPs) within each cluster will be
               more similar after clustering. Performance evaluation using both
               simulated data and real data show that the extended kernelbased
               fingerprinting localization using clustering and principal
               component compression have better location estimation accuracy
               and less computational load.},
  pdf       = {Fusion16_Duan_Fingerprinting.pdf}
}

@inproceedings{Fusion16_Faion,
  author    = {Florian Faion and Maxim Dolgov and Antonio Zea and Uwe D. Hanebeck},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  title     = {{Closed-form Bias Reduction for Shape Estimation with Polygon Models}},
  year      = {2016},
  address   = {Heidelberg, Germany},
  month     = jul,
  abstract  = {We look at the task of estimating the parameters
               of a geometric constraint from noisy points in 2D. The classical
               approach of minimizing the Euclidean distance error between
               points and constraint generally yields biased estimates for
               non-linear constraints and higher noise levels. To deal with this
               issue, the expected distribution of the distance error can be
               explicitly incorporated in the estimator. However, for piecewise
               linear constraints, e.g., polygons, only computationally
               demanding sampling-based approaches are available. We propose two
               major contributions in order to resolve this issue. First, we
               derive closed-form expressions for the probability density of the
               signed distance between noisy points and a polygon angle. Second,
               based on this result, we develop a bias reduction method for
               polygons, which can be calculated in closed-form as well. We
               demonstrate that the quality of our approach can compete with
               its sampling-based alternatives, but only demands a fraction of
               their computational cost.},
  pdf       = {Fusion16_Faion.pdf}
}

@inproceedings{Fusion16_Gilitschenski,
  author    = {Igor Gilitschenski and Gerhard Kurz and Uwe D. Hanebeck and Roland Siegwart},
  title     = {{Optimal Quantization of Circular Distributions}},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  year      = {2016},
  address   = {Heidelberg, Germany},
  month     = jul,
  pdf       = {Fusion16_Quantization.pdf},
  abstract  = {In this work, a new method for approximating
               circular probability distributions by a mixture of weighted
               discrete samples is proposed. Particularly, the wrapped normal
               distribution, the von Mises distribution, and the Bingham distribution
               are considered. The approximation approach is based on
               formulating a quantizer and a global optimality measure, which
               can be optimized directly. Furthermore, a relationship between
               the Bingham distribution and the von Mises distribution are
               formulated showing that it is sufficient to approximate a von
               Mises distribution with suitably chosen parameters in order to
               obtain an optimal approximation of the Bingham distribution.
               The resulting approximation is of particular interest for filtering
               applications, because the involved optimality measure gives rise to
               a general error estimate in propagation of uncertainties through
               nontrivial functions in the circular domain.}
}

@inproceedings{Fusion16_Hanebeck,
  author    = {Uwe D. Hanebeck and Martin Pander},
  title     = {{Progressive Bayesian Estimation with Deterministic Particles}},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  year      = {2016},
  address   = {Heidelberg, Germany},
  month     = jul,
  pdf       = {Fusion16_Hanebeck.pdf},
  abstract  = {This paper introduces an enhanced
               method for progressive Bayesian estimation based
               on a set of deterministic samples. The information
               of a given measurement is gradually introduced in
               order to avoid particle degeneration, which is usually
               encountered in standard particle filters. The main
               contribution of this paper is to derive a new method
               for exploiting smoothness assumptions about the
               unknown underlying density function of the state.}
}

@inproceedings{Fusion16_Kurz-Control,
  author    = {Gerhard Kurz and Maxim Dolgov and Uwe D. Hanebeck},
  title     = {{Progressive Closed-Loop Chance-Constrained Control}},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  year      = {2016},
  address   = {Heidelberg, Germany},
  month     = jul,
  pdf       = {Fusion16_Kurz-ChanceConstraints.pdf},
  abstract  = {Chance-constrained control is a difficult problem
               even if the considered system dynamics are linear. The difficulty
               stems from the facts that the chance constraints are difficult
               to evaluate and that the control law is nonlinear due to the
               constraints. In this paper, we present a novel approach to chanceconstrained
               control, where we solve the unconstrained control
               problem first and then use a progressive method to gradually
               introduce the chance constraints. This has significant advantages
               compared to traditional methods, because we do not require a
               feasible initial solution for the numerical optimization algorithm.
               Finally, we evaluate the novel method and compare it to an
               approach from literature.}
}

@inproceedings{Fusion16_Kurz,
  author    = {Gerhard Kurz and Florian Pfaff and Uwe D. Hanebeck},
  title     = {{Kullback-Leibler Divergence and Moment Matching for Hyperspherical Probability Distributions}},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  year      = {2016},
  address   = {Heidelberg, Germany},
  month     = jul,
  pdf       = {Fusion16_Kurz-KLD2.pdf},
  abstract  = {When approximating one probability density with
               another density, it is desirable to minimize the information loss
               of the approximation as quantified by, e.g., the Kullback–Leibler
               divergence (KLD). It has been known for some time that in the
               case of the Gaussian distribution, matching the first two moments
               of the original density yields the optimal approximation in terms
               of minimizing the KLD. In this paper, we will show that a similar
               property can be proven for certain hyperspherical probability
               distributions, namely the von Mises–Fisher and the Watson
               distribution. This result has profound implications for momentbased
               filtering on the unit hypersphere as it shows that momentbased
               approaches are optimal in the information-theoretic sense.}
}

@inproceedings{Fusion16_Noack,
  author    = {Benjamin Noack and Florian Pfaff and Marcus Baum and Uwe D. Hanebeck},
  title     = {{State Estimation Considering Negative Information with Switching Kalman and Ellipsoidal Filtering}},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  year      = {2016},
  address   = {Heidelberg, Germany},
  month     = jul,
  pdf       = {Fusion16_Noack.pdf},
  abstract  = {State estimation concepts like the Kalman filter heavily rely on potentially noisy sensor data.
               In general, the estimation quality depends on the amount of sensor data that can be exploited.
               However, missing observations do not necessarily impair the estimation quality but may also convey
               exploitable information on the system state. This type of information - noted as negative
               information - often requires specific measurement and noise models in order to take advantage of it.
               In this paper, a hybrid Kalman filter concept is employed that allows using both stochastic and
               set-membership representations of information. In particular, the latter representation is
               intended to account for negative information, which can often be easily described as a bounded
               set in the measurement space. Depending on the type of information, the filtering step of the
               proposed estimator adaptively switches between Gaussian and ellipsoidal noise representations.
               A target tracking scenario is studied to evaluate and discuss the proposed concept.}
}

@inproceedings{Fusion16_Pfaff,
  author    = {Florian Pfaff and Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Nonlinear Prediction for Circular Filtering Using Fourier Series}},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  year      = {2016},
  address   = {Heidelberg, Germany},
  month     = jul,
  pdf       = {Fusion16_nonlinearPred.pdf},
  abstract  = {While nonlinear filtering for circular quantities is
               closely related to nonlinear filtering on linear domains, the
               underlying manifold enables the development of novel filters that
               take advantage of the boundedness of the domain. Previously,
               we introduced Fourier filters that approximate the density or its
               square root using Fourier series. For these filters, we proposed
               filter steps for arbitrary likelihoods and prediction steps for
               the identity system model with additive noise. This paper adds
               the capability of handling arbitrary transition densities in the
               prediction step, which facilitates, e.g., the use of the filters for
               nonlinear systems with additive noise. In the evaluation, the new
               prediction steps for the Fourier filters outperform an SIR particle
               filter, a grid filter, and a nonlinear variant of the von Mises filter.}
}

@inproceedings{Fusion16_Zea,
  author    = {Antonio Zea and Florian Faion and Uwe D. Hanebeck},
  title     = {{Tracking Elongated Extended Objects Using Splines}},
  booktitle = {Proceedings of the 19th International Conference on Information Fusion (Fusion 2016)},
  year      = {2016},
  pdf       = {Fusion16_Zea.pdf},
  address   = {Heidelberg, Germany},
  month     = jul
}

@article{PowTec16_Pieper,
  title    = {{Numerical Modeling of an Automated Optical Belt Sorter Using the Discrete Element Method}},
  author   = {Christoph Pieper and Georg Maier and Florian Pfaff and Harald Kruggel-Emden and Siegmar Wirtz and Robin Gruna and Benjamin Noack and Viktor Scherer and Thomas L{\"{a}}ngle and J{\"{u}}rgen Beyerer and Uwe D. Hanebeck},
  journal  = {Powder Technology},
  year     = {2016},
  month    = jul,
  abstract = {Optical sorters are important devices in the processing and handling
              of the globally growing material streams. The precise optical sorting of many
              bulk solids is still difficult due to the great technical effort necessary
              for transport and flow control. In this study, particle separation with an automated
              optical belt sorter is modeled numerically. The Discrete Element Method (DEM) is used
              to model the sorter and calculate the particle movement as well as
              particle – particle and particle – wall interactions. The particle ejection stage with
              air valves is described with the help of a MATLAB script utilizing particle movement information
              obtained with the DEM. Two models for predicting the particle movement between
              the detection and separation phase are implemented and compared. In the first model,
              it is assumed that the particles are moving with belt velocity and without any cross movements
              and a conventional line scan camera is used for particle detection. In the second model,
              a more sophisticated approach is employed where the particle motion is predicted with
              an area scan camera combined with a tracking algorithm. In addition, the influence of
              different operating parameters like particle shape or conveyor belt length on the
              separation quality of the system is investigated. Results show that numerical simulations
              can offer detailed insight into the operation performance of optical sorters and help to
              optimize operating parameters. The area scan camera approach was found to be superior to
              the standard line scan camera model in almost all investigated categories.},
  doi      = {10.1016/j.powtec.2016.07.018},
  url      = {https://dx.doi.org/10.1016/j.powtec.2016.07.018}
}

@inproceedings{ECC16_Chlebek,
  author    = {Christof Chlebek and Uwe D. Hanebeck},
  title     = {{Approximation of Stochastic Nonlinear Closed-Loop Feedback Control with Application to Miniature Walking Robots}},
  booktitle = {Proceedings of the 2016 European Control Conference (ECC 2016)},
  year      = {2016},
  address   = {Aalborg, Denmark},
  month     = jun,
  pdf       = {ECC16_Chlebek.pdf},
  abstract  = {We consider stochastic nonlinear time-variant systems
               with imperfect state information in the context of model
               predictive control. The optimal control performance can only be
               achieved by closed-loop policies that anticipate future behavior.
               However, the computation of these policies is in general not
               tractable due to the presence of the dual effect, i.e., the control
               actions not only influence the state but also the uncertainty
               of its estimate. Thus, we propose an approximation to closedloop
               control. We use a forward calculation approach, which is
               derived from an open-loop feedback (OLF) control setup, but
               implements the fundamental property of closed-loop control
               that future measurement feedback is considered in the optimization.
               By using a finite set of representative measurements,
               the feedback behavior is anticipated only on basis of current
               information. An optimization based on a continuation method
               implements an effective calculation to obtain a sequence of
               control inputs. The effectiveness of the presented approach is
               demonstrated by means of a miniature walking robot.}
}

@inproceedings{ECC16_Dolgov,
  author    = {Maxim Dolgov and Christof Chlebek and Uwe D. Hanebeck},
  title     = {{Dynamic Compensation of Markov Jump Linear Systems without Mode Observation}},
  booktitle = {Proceedings of the 2016 European Control Conference (ECC 2016)},
  year      = {2016},
  address   = {Aalborg, Denmark},
  month     = jun,
  pdf       = {ECC16_Dolgov.pdf},
  abstract  = {In this paper, we address control of Markov Jump Linear Systems
               via dynamic output feedback without mode observation. Because the optimal
               nonlinear control law for this problem is intractable, we assume a linear
               controller. Under this assumption, the control law computation can be
               expressed in terms of an optimization problem that involves Bilinear Matrix
               Inequalities. Unfortunately, this problem is NP-hard. Thus, we propose an
               alternative iterative algorithm and demonstrate it in a numerical example.
               To the best of our knowledge, the paper presents the first solution of
               mode-independent dynamic compensation of Markov Jump Linear Systems.}
}

@article{JAIF16_Symmetric_S2KF_Steinbring,
  author   = {Jannik Steinbring and Martin Pander and Uwe D. Hanebeck},
  title    = {{The Smart Sampling Kalman Filter with Symmetric Samples}},
  year     = {2016},
  month    = jun,
  volume   = {11},
  number   = {1},
  pages    = {71--90},
  journal  = {Journal of Advances in Information Fusion},
  pdf      = {JAIF16_Steinbring.pdf},
  abstract = {Nonlinear Kalman Filters (KFs) are powerful and widely-used techniques when trying to estimate the hidden state of a stochastic nonlinear dynamic system. A novel sample-based KF is the Smart Sampling Kalman Filter (S²KF). It is based on deterministic Gaussian samples which are obtained from an offline optimization procedure. Although this sampling technique is quite effective, it does not preserve the point symmetry of the Gaussian distribution. In this paper, we overcome this issue by extending the S²KF with a new point-symmetric Gaussian sampling scheme to improve its estimation quality. Moreover, we also improve the numerical stability of the sample computation. This allows us to accurately approximate thousand-dimensional Gaussian distributions using tens of thousands of optimally placed and equally weighted samples. We evaluate the new symmetric S²KF by computing higher-order moments of standard normal distributions and investigate the estimation quality of the S²KF when dealing with symmetric measurement equations. Additionally, extended object tracking based on many measurements per time step is considered. This high-dimensional estimation problem shows the advantage of the S²KF being able to use an arbitrary number of samples independent of the state dimension, in contrast to other state-of-the-art sample-based Kalman Filters. Finally, other estimators also relying on the S²KF’s Gaussian sampling technique, e.g., the Progressive Gaussian Filter (PGF), will benefit from the new point-symmetric sampling as well.}
}

@article{SPL16_Kurz,
  author   = {Gerhard Kurz and Igor Gilitschenski and Uwe D. Hanebeck},
  journal  = {IEEE Signal Processing Letters},
  title    = {{Unscented von Mises--Fisher Filtering}},
  year     = {2016},
  month    = apr,
  number   = {4},
  pages    = {463-467},
  volume   = {23},
  abstract = {We introduce the unscented von Mises–Fisher filter (UvMFF), a nonlinear filtering algorithm for dynamic state estimation on the n-dimensional unit hypersphere. Estimation problems on the unit hypersphere occur in computer vision, e.g., when using omnidirectional cameras, as well as in signal processing. As approaches in literature are limited to very simple system and measurement models, we propose a deterministic sampling scheme on the unit hypersphere, which allows us to handle nonlinear system and measurement models. The proposed approach can be seen as a hyperspherical variant of the unscented Kalman filter (UKF). The advantages of the novel method are shown by means of simulations.},
  doi      = {10.1109/LSP.2016.2529854},
  pdf      = {Kurz-UnscentedVMF.pdf}
}

@article{AES16_Kurz,
  author   = {Gerhard Kurz and Igor Gilitschenski and Uwe D. Hanebeck},
  title    = {{Recursive Bayesian Filtering in Circular State Spaces}},
  month    = mar,
  volume   = {31},
  number   = {3},
  pages    = {70-87},
  pdf      = {AES16_Kurz.pdf},
  journal  = {IEEE Aerospace and Electronic Systems Magazine},
  year     = {2016},
  abstract = {To facilitate recursive state estimation in the circular domain based on circular statistics, we introduce a general framework for estimation of a circular state based on different circular distributions. Specifically, we consider the wrapped normal (WN) distribution and the von Mises distribution. We propose an estimation method for circular systems with nonlinear system and measurement functions. This is achieved by relying on efficient deterministic sampling techniques. Furthermore, we show how the calculations can be simplified in a variety of important special cases, such as systems with additive noise, as well as identity system or measurement functions, which are illustrated using an example from aeronautics. We introduce several novel key components, particularly a distribution-free prediction algorithm, a new and superior formula for the multiplication of WN densities, and the ability to deal with nonadditive system noise. All proposed methods are thoroughly evaluated and compared with several state-of-the-art approaches.},
  doi      = {10.1109/MAES.2016.150083}
}

@article{TM16_Pfaff,
  title    = {{Improving Optical Sorting of Bulk Materials Using Sophisticated Motion Models}},
  author   = {Florian Pfaff and Christoph Pieper and Georg Maier and Benjamin Noack and Harald Kruggel-Emden and Robin Gruna and Uwe D. Hanebeck and Siegmar Wirtz and Viktor Scherer and Thomas L{\"{a}}ngle and J{\"{u}}rgen Beyerer},
  journal  = {tm -- Technisches Messen, De Gruyter},
  number   = {2},
  pages    = {77--84},
  volume   = {83},
  year     = {2016},
  month    = feb,
  abstract = {Visual properties are powerful features to
              reliably classify bulk materials, thereby allowing to detect
              defect or low quality particles. Optical belt sorters are
              an established technology to sort based on these properties,
              but they suffer from delays between the simultaneous
              classification and localization step and the subsequent
              separation step. Therefore, accurate models to predict the
              particles’ motions are a necessity to bridge this gap. In
              this paper, we explicate our concept to use sophisticated
              simulations to derive accurate models and optimize the
              flow of bulk solids via adjustments of the sorter design.
              This allows us to improve overall sorting accuracy and
              cost efficiency. Lastly, initial results are presented.},
  doi      = {/10.1515/teme-2015-0108},
  url      = {https://www.degruyter.com/view/j/teme.2016.83.issue-2/teme-2015-0108/teme-2015-0108.xml},
  pdf      = {TM16_Pfaff.pdf}
}

@article{TAC16_Gilitschenski,
  author   = {Igor Gilitschenski and Gerhard Kurz and Simon J. Julier and Uwe D. Hanebeck},
  title    = {{Unscented Orientation Estimation Based on the Bingham Distribution}},
  journal  = {IEEE Transactions on Automatic Control},
  number   = {1},
  pages    = {172-177},
  volume   = {61},
  doi      = {10.1109/TAC.2015.2423831},
  month    = jan,
  year     = {2016},
  abstract = {In this work, we develop a recursive filter to estimate
              orientation in 3D, represented by quaternions, using directional distributions.
              Many closed-form orientation estimation algorithms are based on
              traditional nonlinear filtering techniques, such as the extended Kalman
              filter (EKF) or the unscented Kalman filter (UKF). These approaches
              assume the uncertainties in the system state and measurements to be
              Gaussian-distributed. However, Gaussians cannot account for the periodic
              nature of the manifold of orientations and thus small angular errors
              have to be assumed and ad hoc fixes must be used. In this work,
              we develop computationally efficient recursive estimators that use the
              Bingham distribution. This distribution is defined on the hypersphere
              and is inherently more suitable for periodic problems. As a result,
              these algorithms are able to consistently estimate orientation even in the
              presence of large angular errors. Furthermore, handling of nontrivial
              system functions is performed using an entirely deterministic method
              which avoids any random sampling. A scheme reminiscent of the UKF
              is proposed for the nonlinear manifold of orientations. It is the first
              deterministic sampling scheme that truly reflects the nonlinear manifold
              of orientations.},
  pdf      = {TAC16_Gilitschenski.pdf}
}

@inproceedings{CDC15_Kurz,
  author    = {Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Parameter Estimation for the Bivariate Wrapped Normal Distribution}},
  booktitle = {Proceedings of the 54th IEEE Conference on Decision and Control (CDC 2015)},
  year      = {2015},
  address   = {Osaka, Japan},
  month     = dec,
  abstract  = {Correlated uncertain angular quantities can be
               modeled using the bivariate wrapped normal distribution.
               In this paper, we focus on the problem of estimating the
               distribution's parameters from a given set of samples. For
               this purpose, we propose several new parameter estimation
               methods and compare them to estimation techniques found in
               literature. All methods are thoroughly evaluated in simulations.
               One of the novel methods is shown to combine the advantages
               of maximum likelihood estimation and moment-based methods,
               thus outperforming current state-of-the-art techniques.},
  pdf       = {CDC15_Kurz.pdf}
}

@inproceedings{CDC15_Dolgov,
  author    = {Maxim Dolgov and Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Chance-constrained Model Predictive Control based on Box Approximations}},
  booktitle = {Proceedings of the 54th IEEE Conference on Decision and Control (CDC 2015)},
  year      = {2015},
  address   = {Osaka, Japan},
  month     = dec,
  abstract  = {In this paper, we consider finite-horizon predictive
               control of linear stochastic systems with chance constraints
               where the admissible region is a convex polytope. For this
               problem, we present a novel solution approach based on box
               approximations. The key notion of our approach consists of
               two steps. First, we apply a linear operation to the joint
               state probability density function such that its covariance is
               transformed into an identity matrix. This operation also defines
               the transformation of the state space and therefore, of the
               admissible polytope. Second, we approximate the admissible
               region from the inside using axis-aligned boxes. By doing so, we
               obtain a conservative approximation of the constraint violation
               probability virtually in closed form (the expression contains
               Gaussian error functions). The presented control approach is
               demonstrated in a numerical example.},
  pdf       = {CDC15_Dolgov.pdf}
}

@incollection{CRC15_Sijs,
  title     = {Event-Based Control and Signal Processing},
  author    = {Joris Sijs and Benjamin Noack and Mircea Lazar and Uwe D. Hanebeck},
  chapter   = {{Time-Periodic State Estimation with Event-Based Measurement Updates}},
  editor    = {Marek Miskowicz},
  pages     = {261--279},
  publisher = {CRC Press},
  year      = {2015},
  month     = nov,
  abstract  = {To reduce the amount of data transfers
               in networked systems, measurements can be taken at
               an event on the sensor value rather than periodically
               in time. Yet, this could lead to a divergence of estimation results when only the received measurement
               values are exploited in a state estimation procedure.
               A solution to this issue has been found by developing
               estimators that perform a state update at both the event
               instants as well as periodically in time: when an event
               occurs the estimated state is updated using the measurement received, while at periodic instants the update is
               based on knowledge that the sensor value lies within a
               bounded subset of the measurement space. Several solutions for event-based state estimation will be presented
               in this chapter, either based on stochastic representations
               of random vectors, on deterministic representations of
               random vectors or on a mixture of the two. All solutions aim to limit the required computational resources
               by deriving explicit solutions for computing estimation
               results. Yet, the main achievement for each estimation
               solution is that stability of the estimation results are (not directly) dependent on the employed event sampling
               strategy. As such, changing the event sampling strategy does not imply to change the event-based estimator
               as well. This aspect is also illustrated in a case study
               of tracking the distribution of a chemical compound
               effected by wind via a wireless sensor network.},
  url       = {https://doi.org/10.1201/b19013}
}

@article{TAES15_Kurz,
  author   = {Gerhard Kurz and Uwe D. Hanebeck},
  title    = {{Trigonometric Moment Matching and Minimization of the Kullback--Leibler Divergence}},
  journal  = {IEEE Transactions on Aerospace and Electronic Systems},
  number   = {1},
  pages    = {3480-3484},
  volume   = {51},
  doi      = {10.1109/TAES.2015.150406},
  month    = oct,
  year     = {2015},
  abstract = {We show an important property of the von Mises
              distribution on the unit circle. If we approximate an arbitrary
              circular distribution using a von Mises distribution, the result
              obtained by trigonometric moment matching also minimizes
              the Kullback–Leibler divergence (Theorem 1). This result is a
              justification for circular filtering algorithms based on trigonometric
              moment matching as the loss of information is minimized.
              Furthermore, we show that Theorem 1 does not hold for the
              wrapped normal distribution.},
  pdf      = {TAES15_Kurz.pdf}
}

@article{SPL15_Baum,
  author  = {Marcus Baum and Peter Willett and Uwe D. Hanebeck},
  title   = {{On Wasserstein Barycenters and MMOSPA Estimation}},
  journal = {IEEE Signal Processing Letters},
  year    = {2015},
  month   = oct,
  url     = {https://ieeexplore.ieee.org/document/7054438/},
  number  = {10},
  pages   = {1511-1515},
  volume  = {22},
  doi     = {10.1109/LSP.2015.2410217}
}

@inproceedings{SDF15_Faion,
  author    = {Florian Faion and Antonio Zea and Jannik Steinbring and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Recursive Bayesian Pose and Shape Estimation of 3D Objects Using Transformed Plane Curves}},
  booktitle = {{Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2015)}},
  year      = {2015},
  address   = {Bonn, Germany},
  month     = oct,
  abstract  = {We consider the task of recursively estimating the
               pose and shape parameters of 3D objects based on noisy point
               cloud measurements from their surface. We focus on objects
               whose surface can be constructed by transforming a plane curve,
               such as a cylinder that is constructed by extruding a circle.
               However, designing estimators for such objects is challenging,
               as the straightforward distance-minimizing approach cannot
               observe all parameters, and additionally is subject to bias in
               the presence of noise. In this article, we first discuss these issues
               and then develop probabilistic models for cylinder, torus, cone,
               and an extruded curve by adapting related approaches including
               Random Hypersurface Models, partial likelihood, and symmetric
               shape models. In experiments with simulated data, we show that
               these models yield unbiased estimators for all parameters even
               in the presence of high noise.},
  pdf       = {SDF15_Faion.pdf}
}

@inproceedings{SDF15_Kurz,
  author    = {Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Stochastic Sampling of the Hyperspherical von Mises--Fisher Distribution Without Rejection Methods}},
  booktitle = {{Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2015)}},
  year      = {2015},
  address   = {Bonn, Germany},
  month     = oct,
  abstract  = {We propose a novel sampling algorithm for the
               von Mises–Fisher distribution on the unit hypersphere. Unlike
               previous works, we show a solution for an arbitrary number of
               dimensions without requiring rejection sampling. As a result, the
               proposed algorithm has a deterministic runtime. The key idea
               consists in applying the inversion method to a one-dimensional
               subproblem and analytically calculating the integral occurring in
               the distribution function. The proposed method is most efficient
               for odd numbers of dimensions. We compare the algorithm to a
               state-of-the-art rejection sampling method in simulations.},
  pdf       = {SDF15_Kurz-VMFSampling.pdf}
}

@inproceedings{MFI15_Baum,
  author    = {Marcus Baum and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Kalman Filter-based SLAM with Unknown Data Association using Symmetric Measurement Equations}},
  booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015)},
  year      = {2015},
  address   = {San Diego, California, USA},
  month     = sep,
  abstract  = {This work investigates a novel method for dealing
               with unknown data associations in Kalman filter-based
               Simultaneous Localization and Mapping (SLAM) problems. The key
               idea is to employ the concept of Symmetric Measurement
               Equations (SMEs) in order to remove the data association
               uncertainty from the original measurement equation. Based
               on the resulting modified measurement equation, standard
               nonlinear Kalman filters can estimate the full joint state vector
               of the robot and landmarks without explicitly calculating data
               association hypotheses.},
  pdf       = {MFI15_Baum.pdf}
}

@inproceedings{MFI15_Faion,
  author    = {Florian Faion and Marcus Baum and Antonio Zea and Uwe D. Hanebeck},
  title     = {{Depth Sensor Calibration by Tracking an Extended Object}},
  booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015)},
  year      = {2015},
  address   = {San Diego, California, USA},
  month     = sep,
  abstract  = {In this paper, we propose a novel algorithm for automatically
               calibrating a network of depth sensors, based on a moving calibration object.
               The sensors may have non-overlapping fields of view in order to avoid interference.
               Two major challenges are discussed. First, depending on where the object is
               located relative to the sensor, the number and quality of the measurements strongly varies.
               Second, a single depth sensor observes the calibration object only from one side.
               Dealing with these challenges requires a simple calibration object as
               well as an algorithm that can deal with under-determined measurements of
               varying quality. A recursive Bayesian estimator is developed that determines the
               extrinsic parameters by measuring the surface of a moving cube with
               known pose. Our approach does not restrict the configuration of the network and
               requires no manual initialization or interaction.
               Ambiguities that are induced by the rotational cube symmetries are
               resolved by applying a multiple model approach. Besides synthetic evaluation we
               perform real data experiments and compare to state-of-the-art calibration.},
  pdf       = {MFI15_Faion.pdf}
}

@inproceedings{MFI15_Gilitschenski,
  author    = {Igor Gilitschenski and Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{A Stochastic Filter for Planar Rigid-Body Motions}},
  booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015)},
  year      = {2015},
  address   = {San Diego, California, USA},
  month     = sep,
  abstract  = {This paper presents a novel algorithm for the
               estimation of planar rigid-body motions. It is based on using a
               probability distribution that is inherently defined on the non-
               linear manifold representing these motions and on proposing
               a deterministic sampling scheme that makes consideration of
               complicated system models possible. Furthermore, we show
               that the measurement update for a manifold equivalent to
               noisy direct measurements can be carried out in closed form.
               Thus, the resulting method avoids errors made due to local
               linearization and outperforms methods that wrongly assume
               Gaussian distributions, which we show by comparing the
               proposed filter to the UKF.},
  pdf       = {MFI15_Gilitschenski.pdf}
}

@inproceedings{MFI15_Hanebeck,
  author    = {Uwe D. Hanebeck},
  title     = {{Bayesian Fusion of Empirical Distributions Based on Local Density Reconstruction}},
  booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015)},
  year      = {2015},
  address   = {San Diego, California, USA},
  month     = sep,
  abstract  = {Fusing two random vectors is simple, when they are characterized by continuous probability functions.
               According to Bayes' law, fusion then consists of multiplying the two densities. When only empirical distributions
               are given and a resulting empirical distribution is desired, Bayes' law is no longer applicable. Obviously,
               fusion could now be performed by reconstructing the underlying continuous densities, subsequent multiplication,
               and sampling of the result. As this is overly complicated, our goal is to perform a direct Bayesian fusion of the
               two given empirical distributions. We devise a generalized multiplication procedure that mutually reweights appropriate
               points of one density by local density values of the other density. The density values are efficiently estimated locally
               by nearest neighbor operations. The method is symmetric in the sense that it uses points from both densities.},
  pdf       = {MFI15_Hanebeck.pdf}
}

@inproceedings{MFI15_Kurz,
  author    = {Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Toroidal Information Fusion Based on the Bivariate von Mises Distribution}},
  booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015)},
  year      = {2015},
  address   = {San Diego, California, USA},
  month     = sep,
  abstract  = {Fusion of toroidal information, such as correlated
               angles, is a problem that arises in many fields ranging from
               robotics and signal processing to meteorology and bioinfor-
               matics. For this purpose, we propose a novel fusion method
               based on the bivariate von Mises distribution. Unlike most
               literature on the bivariate von Mises distribution, we consider
               the full version with matrix-valued parameter rather than
               a simplified version. By doing so. we are able to derive
               the exact analytical computation of the fusion operation. We
               also propose an efficient approximation of the normalization
               constant including an error bound and present a parameter
               estimation algorithm based on a maximum likelihood approach.
               The presented algorithms are illustrated through examples.},
  pdf       = {MFI15_Kurz.pdf},
  annote    = {<b>Nominee Best Paper Award</b> <a href="https://isas.iar.kit.edu/pdf/award/Mfi2015_award_nominee.pdf" target="_blank">Certificate (PDF)</a>}
}

@inproceedings{MFI15_Noack,
  author    = {Benjamin Noack and Marcus Baum and Uwe D. Hanebeck},
  title     = {{State Estimation for Ellipsoidally Constrained Dynamic Systems with Set-Membership Pseudo Measurements}},
  booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015)},
  year      = {2015},
  address   = {San Diego, California, USA},
  month     = sep,
  abstract  = {In many dynamic systems, the evolution of the
               state is subject to specific constraints. In general, constraints
               cannot easily be integrated into the prediction-correction structure
               of the Kalman filter algorithm. Linear equality constraints
               are an exception to this rule and have been widely used and
               studied as they allow for simple closed-form expressions. A
               common approach is to reformulate equality constraints into
               pseudo measurements of the state to be estimated. However,
               equality constraints define deterministic relationships between
               state components which is an undesirable property in Kalman
               filtering as this leads to singular covariance matrices. A second
               problem relates to the knowledge required to identify and
               define precise constraints, which are met by the system state.
               In this article, ellipsoidal constraints are introduced that can
               be employed to model a bounded region, to which the system
               state is constrained. This concept constitutes an easy-to-use
               relaxation of equality constraints. In order to integrate ellipsoidal
               constraints into the Kalman filter structure, a generalized filter
               framework is utilized that relies on a combined stochastic and
               set-membership uncertainty representation.},
  pdf       = {MFI15_Noack.pdf}
}

@inproceedings{MFI15_Pfaff,
  author    = {Florian Pfaff and Marcus Baum and Benjamin Noack and Uwe D. Hanebeck and Robin Gruna and Thomas L{\"{a}}ngle and J{\"{u}}rgen Beyerer},
  title     = {{TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials}},
  booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015)},
  year      = {2015},
  address   = {San Diego, California, USA},
  month     = sep,
  abstract  = {Optical belt sorters are a versatile, state-of-the-art technology to sort
               bulk materials that are hard to sort based on only nonvisual properties. In
               this paper, we propose an extension to current optical belt sorters that
               involves replacing the line camera with an area camera to observe a wider
               field of view, allowing us to observe each particle over multiple time
               steps. By performing multitarget tracking, we are able to improve the
               prediction of each particle's movement and thus enhance the performance of
               the utilized separation mechanism. We show that our approach will allow
               belt sorters to handle new classes of bulk materials while improving cost
               efficiency. Furthermore, we lay out additional extensions that are made
               possible by our new paradigm},
  pdf       = {MFI15_Pfaff.pdf}
}

@inproceedings{MFI15_Steinbring,
  author    = {Jannik Steinbring and Marcus Baum and Antonio Zea and Florian Faion and Uwe D. Hanebeck},
  title     = {{A Closed-Form Likelihood for Particle Filters to Track Extended Objects with Star-Convex RHMs}},
  booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015)},
  year      = {2015},
  address   = {San Diego, California, USA},
  month     = sep,
  abstract  = {Modeling 2D extended targets with star-convex Random Hypersurface Models (RHMs) allows for
               accurate object pose and shape estimation. A star-convex RHM models the shape of an object with the aid of
               a radial function that describes the distance from the object center to any point on its boundary. However,
               up to now only linear estimators, i.e., Kalman Filters, are used due to the lack of a explicit likelihood function.
               In this paper, we propose a closed-form and easy to implement likelihood function for tracking extended targets
               with star-convex RHMs. This makes it possible to apply nonlinear estimators such as Particle Filters to estimate
               a detailed shape of a target. We compared the proposed likelihood against the usual Kalman filter approaches
               with tracking pose and shape of an airplane in 2D. The evaluations showed that the combination of the
               Progressive Gaussian Filter (PGF) and the new likelihood function delivers the best estimation performance
               and can outperform the usually employed Kalman Filters.},
  pdf       = {MFI15_Steinbring.pdf}
}

@inproceedings{MFI15_Zea,
  author    = {Antonio Zea and Florian Faion and Uwe D. Hanebeck},
  title     = {{Shape Tracking using Partial Information Models}},
  booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015)},
  year      = {2015},
  address   = {San Diego, California, USA},
  month     = sep,
  abstract  = {One of the challenges in shape tracking is how
               to deal with associating measurements to sources in the shape,
               while also taking to account parameters such as shape curvature
               and noise characteristics. Partial Information Models (PIMs)
               introduce a new approach that addresses this issue. The idea
               is to reparametrize each measurement into two components,
               one which depends on the position of its source on the shape,
               and another which depends on how well it fits in the shape.
               This allows for the derivation of a partial likelihood which
               combines the strengths of probabilistic approaches and distance
               minimization techniques. We propose an implementation of
               PIMs using level-sets, which allow for a close approximation
               of the distribution of distances we expect for a given shape. In
               turn, this can be used to develop estimators that are highly
               robust against high noise and occlusions.},
  pdf       = {MFI15_Zea.pdf}
}

@article{SPL15_Reinhardt,
  author   = {Marc Reinhardt and Benjamin Noack and Pablo O. Arambel and Uwe D. Hanebeck},
  title    = {{Minimum Covariance Bounds for the Fusion under Unknown Correlations}},
  journal  = {IEEE Signal Processing Letters},
  volume   = {22},
  number   = {9},
  pages    = {1210--1214},
  month    = sep,
  year     = {2015},
  abstract = {One of the key challenges in distributed linear estimation
              is the systematic fusion of estimates. While the fusion gains
              that minimize the mean squared error of the fused estimate for
              known correlations have been established, no analogous statement
              could be obtained so far for unknown correlations. In this contribution,
              we derive the gains that minimize the bound on the true covariance
              of the fused estimate and prove that Covariance Intersection (CI)
              is the optimal bounding algorithm for two estimates under completely
              unknown correlations. When combining three or more variables, the
              CI equations are not necessarily optimal, as shown by a counterexample.},
  doi      = {10.1109/LSP.2015.2390417},
  url      = {https://dx.doi.org/10.1109/LSP.2015.2390417},
  pdf      = {SPL15_Reinhardt.pdf}
}

@incollection{CRC15_Noack,
  booktitle = {Multisensor Data Fusion: From Algorithms and Architectural Design to Applications},
  author    = {Benjamin Noack and Joris Sijs and Marc Reinhardt and Uwe D. Hanebeck},
  title     = {{Treatment of Dependent Information in Multisensor Kalman Filtering and Data Fusion}},
  editor    = {Hassen Fourati},
  pages     = {169--192},
  publisher = {CRC Press},
  year      = {2015},
  month     = aug,
  abstract  = {Distributed and decentralized processing and fusion of sensor data are becoming increasingly important. In view of the Internet of Things and the vision of ubiquitous sensing, designing and implementing multisensor state estimation algorithm have already become a key issue. A network of interconnected sensor devices is usually characterized by the idea to process and collect data locally and independently on the sensor nodes. However, this does not imply that the data are independent of each other, and the state estimation algorithms have to address possible interdependencies so as to avoid erroneous data fusion results.
               Dependencies among local estimates generally can be traced back to common sensor information and common process noise. A wide variety of Kalman filtering schemes allow for the treatment of dependent data in centralized, distributed, and decentralized networks of sensor nodes, but making the right choice is itself dependent upon analyzing and weighing up the different advantages and disadvantages. This chapter discusses different strategies to identify and treat dependencies among Kalman filter estimates while pointing out advantages and challenges.},
  url       = {https://doi.org/10.1201/b18851}
}

@inproceedings{Fusion15_Baum,
  author    = {Marcus Baum and Balakumar Balasingam and Peter Willett and Uwe D. Hanebeck},
  title     = {{OSPA Barycenters for Clustering Set-Valued Data}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {We consider the problem of clustering set-valued observations, i.e., each
               observation is a set that consists of a finite number of real vectors. For this
               purpose, we develop a $k$-means algorithm that employs the OSPA distance for
               measuring the distance between sets. In particular, we introduce a novel
               alternating optimization algorithm for the OSPA barycenter of sets with
               varying cardinalities that is required for calculating cluster centroids
               efficiently. The benefits of clustering set-valued data with respect to the OSPA
               distance are illustrated by means of simulated experiments in the context of
               target tracking and recognition.},
  pdf       = {Fusion15_Baum.pdf}
}

@inproceedings{Fusion15_Chlebek,
  author    = {Christof Chlebek and Uwe D. Hanebeck},
  title     = {{Stochastic Nonlinear Model Predictive Control Based on Deterministic Scenario Generation}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {We consider closed-loop feedback (CLF) stochastic model predictive control of
               nonlinear time-invariant systems with imperfect state information. In this class of
               control problems, future information feedback is considered in the decision making process,
               and thus, the effect of the control influencing the state uncertainty is taken into account.
               The main challenge in the solution is to find a good approximation to the arising stochastic
               dynamic programming problem, which is computationally not tractable. In this work, future
               information is considered in the form of conditional state probability densities. Thus,
               the objective is it to optimize the state and its uncertainty as a combined problem.
               We propose to discretize the state space by a novel scenario generation approach based on
               deterministic sampling. A distance based threshold determines the narrowness of the discretization.
               The dynamic programming problem is formulated such that the approximate cumulative control
               cost function can be explicitly evaluated offline. The online calculation consists of a
               one-step prediction and the interpolation of the explicit cost function in order to calculate
               the control input. The effectiveness of this novel method is presented by means of a simulation.},
  pdf       = {Fusion15_Chlebek.pdf}
}

@inproceedings{Fusion15_Faion,
  author    = {Florian Faion and Antonio Zea and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Partial Likelihood for Unbiased Extended Object Tracking}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {An extended object gives rise to several measurements that originate
               from unknown measurement sources on the object. In this paper, we
               consider the tracking and parameter estimation of extended objects that
               are modeled as a curve in 2D such as a circle or an ellipse. A standard
               model for such extended objects is to assume that the unknown
               measurement sources are uniformly distributed on the curve. We argue
               that the uniform distribution may not be the best choice in scenarios
               where the true distribution of the measurements significantly differs
               from a uniform distribution. Based on results from curve fitting and
               errors-in-variables models, we develop a partial likelihood that ignores
               the distribution of measurement sources and can be shown to outperform
               the likelihood for a uniform distribution in these scenarios. If the
               true measurement sources are in fact uniformly distributed, our new
               likelihood results in a slightly slower convergence but has the same
               asymptotic behavior.},
  pdf       = {Fusion15_Faion.pdf}
}

@inproceedings{Fusion15_Gilitschenski,
  author    = {Igor Gilitschenski and Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Non-Identity Measurement Models for Orientation Estimation Based on Directional Statistics}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {We propose a novel measurement update procedure for orientation
               estimation algorithms that are based on directional statistics. This
               involves consideration of two scenarios, orientation estimation in the
               2D plane and orientation estimation in three-dimensional space. We make
               use of the von Mises distribution and the Bingham distribution in these
               scenarios. In the derivation, we discuss directional counterparts to the
               extended Kalman filter and a statistical-linearization-based filter. The
               newly proposed algorithm makes use of deterministic sampling and can be
               thought of as a directional variant of the measurement update that is
               used in well-known sample-based algorithms such as the unscented Kalman
               filter.},
  pdf       = {Fusion15_Gilitschenski.pdf}
}

@inproceedings{Fusion15_Hanebeck-AssociationFreeTracking,
  author    = {Uwe D. Hanebeck and Marcus Baum},
  title     = {{Association-Free Direct Filtering of Multi-Target Random Finite Sets with Set Distance Measures}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {We consider association-free tracking of multiple targets without identities.
               The uncertain multi-target state and the uncertain measurements cannot be described
               by a random vector as this would imply a certain order. Instead, they are described by
               an unordered random finite set (RFS). Particle-based random finite set densities are used
               for characterizing the RFS in a simple and natural way. For recursive Bayesian filtering,
               optimal multi-target state estimates are calculated by systematically minimizing an appropriate
               set distance measure while directly operating on the particles. Although methods for calculating
               point estimates of random finite set densities based on appropriate distance measures are
               available in literature, the proposed recursive filtering is a novel contribution.},
  pdf       = {Fusion15_Hanebeck-AssociationFreeTracking.pdf}
}

@inproceedings{Fusion15_Hanebeck-Polytopes,
  author    = {Uwe D. Hanebeck and Maxim Dolgov},
  title     = {{Adaptive Lower Bounds for Gaussian Measures of Polytopes}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {In this paper, we address the problem of probability mass
               computation of a multivariate Gaussian contained within a polytope. This
               computation requires an evaluation of a multivariate definite integral
               of the Gaussian, whose solution is not tractable for higher dimensions
               in a reasonable amount of time. Thus, research concentrates on the
               derivation of approximate but sufficiently fast computation methods. We
               propose a novel approach that approximates the underlying integration
               domain, namely the polytope, using disjoint sectors such that the
               probability mass contained within the sectors is maximized. In order to
               derive our main algorithm, we first propose an approach to approximate
               volume computation of a polytope using disjoint sectors. This solution
               is then extended to the computation of the probability mass of a
               Gaussian contained within the polytope. The presented solution provides
               a lower bound on the true probability mass contained within the
               polytope. Because the initial optimization problem is highly nonlinear,
               we propose a greedy algorithm that splits the sectors with the highest
               probability mass.},
  pdf       = {Fusion15_Hanebeck-Polytopes.pdf}
}

@inproceedings{Fusion15_Kurz,
  author    = {Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Heart Phase Estimation Using Directional Statistics for Robotic Beating Heart Surgery}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {Robotic beating heart surgery requires accurate information about the
               current state of the heart. For this purpose, it is of great importance
               to have a good estimate of the heart's current phase, which in essence
               corresponds to the percentage of the current heart cycle that has
               already passed. Estimation of the heart phase is a highly nontrivial
               problem as the heart motion is not exactly periodic. On the contrary, it
               varies slightly from beat to beat and changes in frequency over time. In
               order to derive a robust phase estimation algorithm, we rely on
               directional statistics, a subfield of statistics that deals with
               quantities that are inherently periodic, such as the phase of the
               beating heart. The proposed methods are evaluated on a real data set and
               shown to be superior to the state of the art.},
  pdf       = {Fusion15_Kurz.pdf}
}

@inproceedings{Fusion15_Noack,
  author    = {Benjamin Noack and Simon J. Julier and Uwe D. Hanebeck},
  title     = {{Treatment of Biased and Dependent Sensor Data in Graph-based SLAM}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {A common approach to attack the simultaneous localization and mapping problem (SLAM)
               is to consider factor-graph formulations of the underlying filtering and estimation setup.
               While Kalman filter-based methods provide an estimate for the current pose of a robot and
               all landmark positions, graph-based approaches take not only the current pose into account
               but also the entire trajectory of the robot and have to solve a nonlinear least-squares
               optimization problem. Using graph-based representations has proven to be highly scalable
               and very accurate as compared with traditional filter-based approaches. However, biased
               measurements as well as unmodeled correlations can lead to a sharp deterioration in the
               estimation quality and hence require careful consideration. In this paper, a method to
               incorporate biased or dependent measurement information is proposed that can easily be
               integrated into existing optimization algorithms for graph-based SLAM. For biased sensor
               data, techniques from ellipsoidal calculus are employed to compute the corresponding
               information matrices. Dependencies among noise terms are treated by a generalization
               of the covariance intersection concept. The treatment of both biased and correlated
               sensor data rest upon the inflation of the involved error matrices. Simulations
               are used to discuss and evaluate the proposed method.},
  pdf       = {Fusion15_Noack.pdf}
}

@inproceedings{Fusion15_Pfaff,
  author    = {Florian Pfaff and Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Multimodal Circular Filtering Using Fourier Series}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  pdf       = {Fusion15_Pfaff.pdf},
  abstract  = {Recursive filtering with multimodal likelihoods and transition
               densities on periodic manifolds is, despite the compact domain, still an
               open problem. We propose a novel filter for the circular case that performs
               well compared to other state-of-the-art filters adopted from linear
               domains. The filter uses a limited number of Fourier coefficients of the
               square root of the density. This representation is preserved throughout
               filter and prediction steps and allows obtaining a valid density at any
               point in time. Additionally, analytic formulae for calculating Fourier
               coefficients of the square root of some common circular densities are
               provided. In our evaluation, we show that this new filter performs well in
               both unimodal and multimodal scenarios while requiring only a reasonable
               number of coefficients.}
}

@inproceedings{Fusion15_Steinbring,
  author    = {Jannik Steinbring and Uwe D. Hanebeck},
  title     = {{GPU-Accelerated Progressive Gaussian Filtering with Applications to Extended Object Tracking}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {Since the last years, Graphics Processing Units (GPUs) have massive parallel execution capabilities even
               for non-graphic related applications. The field of nonlinear state estimation is no exception here.
               Particle Filters have already been successfully ported to GPUs. In this paper,
               we propose a GPU-accelerated variant of the Progressive Gaussian Filter (PGF). This allows
               us to combine the advantages of the particle flow with the ability to process thousands of
               measurements at once in order to improve state estimation quality. To get a meaningful
               comparison between its CPU and GPU variants, we additionally propose a likelihood for tracking
               a sphere and its extent in 3D based on noisy point measurements. The likelihood considers the
               physical relationship between sensor, measurement, and sphere to best exploit the information of the received measurements.
               We evaluate the GPU implementation of the PGF using the proposed likelihood in combination with tens of thousands of measurements.
               Although the CPU implementation fully exploits parallelization techniques such as SSE and OpenMP, the GPU-accelerated PGF
               reaches speedups over 20 and real-time tracking can nearly be achieved.},
  pdf       = {Fusion15_Steinbring.pdf}
}

@inproceedings{Fusion15_Zea,
  author    = {Antonio Zea and Florian Faion and Uwe D. Hanebeck},
  title     = {{Exploiting Clutter: Negative Information for Enhanced Extended Object Tracking}},
  booktitle = {Proceedings of the 18th International Conference on Information Fusion (Fusion 2015)},
  year      = {2015},
  address   = {Washington D.C., USA},
  month     = jul,
  abstract  = {Since the last years, Graphics Processing Units (GPUs) have massive parallel execution capabilities even
               for non-graphic related applications. The field of nonlinear state estimation is no exception here.
               Particle Filters have already been successfully ported to GPUs. In this paper,
               we propose a GPU-accelerated variant of the Progressive Gaussian Filter (PGF). This allows
               us to combine the advantages of the particle flow with the ability to process thousands of
               measurements at once in order to improve state estimation quality. To get a meaningful
               comparison between its CPU and GPU variants, we additionally propose a likelihood for tracking
               a sphere and its extent in 3D based on noisy point measurements. The likelihood considers the
               physical relationship between sensor, measurement, and sphere to best exploit the information of the received measurements.
               We evaluate the GPU implementation of the PGF using the proposed likelihood in combination with tens of thousands of measurements.
               Although the CPU implementation fully exploits parallelization techniques such as SSE and OpenMP, the GPU-accelerated PGF
               reaches speedups over 20 and real-time tracking can nearly be achieved.},
  pdf       = {Fusion15_Zea.pdf}
}

@inproceedings{ACC15_Dolgov,
  author    = {Maxim Dolgov and J\"{o}rg Fischer and Uwe D. Hanebeck},
  title     = {Infinite-Horizon Sequence-based Networked Control without Acknowledgments},
  booktitle = {Proceedings of the 2015 American Control Conference (ACC 2015)},
  year      = {2015},
  month     = jul,
  address   = {Chicago, Illinois, USA},
  pdf       = {ACC2015_DolgovFischer.pdf},
  abstract  = {In this paper, we consider infinite-horizon networked LQG control over multipurpose networks that do not provide acknowledgments
               (UDP-like networks). The information communicated over the networks experiences transmission delays and losses that are modeled as stochastic
               processes. In oder to mitigate the delays and losses in the controller-actuator channel, the controller transmits sequences of predicted control inputs
               in addition to the current control input. To be able to mitigate delays and losses in the feedback channel, the estimator computes the estimate using
               M last measurements. In this scenario, the separation principle does not hold and the optimal control law is in general nonlinear. However, assuming
               that the controller and the estimator are linear and possess constant gains, we are able to derive the control law. The presented control law is
               evaluated by means of simulations.}
}

@inproceedings{ACC15_Kurz,
  author    = {Gerhard Kurz and Maxim Dolgov and Uwe D. Hanebeck},
  title     = {{Nonlinear Stochastic Model Predictive Control in the Circular Domain}},
  booktitle = {Proceedings of the 2015 American Control Conference (ACC 2015)},
  year      = {2015},
  month     = jul,
  address   = {Chicago, Illinois, USA},
  pdf       = {ACC2015_KurzDolgov.pdf},
  abstract  = {In this paper, we present an open-loop Stochastic
               Model Predictive Control (SMPC) method for discrete-time
               nonlinear systems whose state is defined on the unit circle.
               This modeling approach allows considering systems that include
               periodicity in a more natural way than standard approaches
               based on linear spaces. The main idea of this work is twofold:
               (i) we model the quantities of the system, i.e., the state, the
               measurements, and the noises, directly as circular quantities
               described by circular probability densities, and (ii) we apply
               deterministic sampling given in closed form to represent the
               occurring densities. The latter allows us to make the prediction
               required for solution of the SMPC problem tractable. We
               evaluate the proposed control scheme by means of simulations.}
}

@article{JAIF15_Faion,
  author   = {Florian Faion and Antonio Zea and Marcus Baum and Uwe D. Hanebeck},
  title    = {{Symmetries in Bayesian Extended Object Tracking}},
  journal  = {Journal of Advances in Information Fusion},
  url      = {https://confcats_isif.s3.amazonaws.com/web-files/journals/entries/433_1_art_9_24888.pdf},
  pages    = {13--30},
  volume   = {10},
  number   = {1},
  month    = jun,
  year     = {2015},
  abstract = {In this work, we exploit geometric symmetries in extended
              objects in order to improve Bayesian tracking algorithms that use
              Spatial Distribution Models, Greedy Association Models as used in
              curve fitting, and Random Hypersurface Models. The key idea is to
              describe symmetric objects by solely modeling the non-redundant
              part of the shape, while the remainder of the shape follows from
              symmetry. Following this idea, we develop simplified versions for
              the three models that take advantage of the symmetry. Exploiting
              symmetries yields two major benefits. First, complex symmetric
              shapes can be equivalently represented by a fraction of the original
              shape parameters. Second, when using sample-based filters, such as
              the widely used Unscented Kalman Filter, symmetry yields a higher
              effective sample resolution. It is worth mentioning that estimating
              even simple objects such as a stick, which only have one reflectional
              symmetry, can be significantly improved.}
}

@article{TSP15_Baum,
  author   = {Marcus Baum and Peter Willett and Uwe D. Hanebeck},
  title    = {{Polynomial-Time Algorithms for the Exact MMOSPA Estimate of a Multi-Object Probability Density Represented by Particles}},
  journal  = {IEEE Transactions on Signal Processing},
  year     = {2015},
  pages    = {2476 -- 2484},
  number   = {10},
  month    = may,
  url      = {https://ieeexplore.ieee.org/document/7041175/},
  doi      = {10.1109/TSP.2015.2403292},
  abstract = {In multi-object estimation, the traditional minimum mean squared error (MMSE) objective
              is unsuitable: a simple permutation of object identities can turn a very good estimate into
              what is apparently a very bad one. Fortunately, a criterion tailored to sets-minimization of
              the mean optimal sub-pattern assignment (MMOSPA)-has recently evolved. Aside from special cases,
              exact MMOSPA estimates have seemed difficult to compute. But in this work we present the first
              exact polynomial-time algorithms for calculating the MMOSPA estimate for probability densities
              that are represented by particles. The key insight is that the MMOSPA estimate can be found by means
              of enumerating the cells of a hyperplane arrangement, which is a traditional problem from computational
              geometry. Although the runtime complexity is still high for the general case, efficient algorithms are
              obtained for two special cases, i.e., (i) two targets with arbitrary state dimensions and (ii) an
              arbitrary number of one-dimensional targets.}
}

@article{AT15_Hanebeck,
  author   = {Uwe D. Hanebeck},
  title    = {{Optimal Reduction of Multivariate Dirac Mixture Densities}},
  journal  = {at -- Automatisierungstechnik, Oldenbourg Verlag},
  volume   = {63},
  number   = {4},
  pages    = {265--278},
  year     = {2015},
  month    = apr,
  doi      = {10.1515/auto-2015-0005},
  url      = {https://dx.doi.org/10.1515/auto-2015-0005},
  abstract = {This paper is concerned with the optimal approximation of a given multivariate Dirac mixture,
              i.e., a density comprising weighted Dirac distributions on a continuous domain, by a Dirac mixture
              with a reduced number of components. The parameters of the approximating density are calculated
              by numerically minimizing a smooth distance measure, a generalization of the well-known
              Cramer–von Mises-Distance to the multivariate case. This generalization is achieved by
              defining an alternative to the classical cumulative distribution, the Localized Cumulative
              Distribution (LCD), as a smooth characterization of discrete random quantities (on continuous domains).
              The resulting approximation method provides the basis for various efficient nonlinear
              estimation and control methods.}
}

@article{JAIF14_Steinbring-S2KF,
  author   = {Jannik Steinbring and Uwe D. Hanebeck},
  title    = {{LRKF Revisited: The Smart Sampling Kalman Filter (S2KF)}},
  journal  = {Journal of Advances in Information Fusion},
  volume   = {9},
  number   = {2},
  pages    = {106--123},
  year     = {2014},
  month    = dec,
  url      = {https://confcats_isif.s3.amazonaws.com/web-files/journals/entries/441_1_art_11_17020.pdf},
  abstract = {An accurate Linear Regression Kalman Filter
              (LRKF) for nonlinear systems called Smart Sampling Kalman
              Filter (S²KF) is introduced. In order to get a better understanding
              of this new filter, a general introduction to Nonlinear Kalman
              Filters based on statistical linearization and LRKFs is given.
              The S²KF is based on a new low-discrepancy Dirac mixture
              approximation of Gaussian densities. This approximation com-
              prises an arbitrary number of optimally and deterministically
              placed samples in the relevant regions of the state space, so
              that the filter resolution can be adapted to either achieve high-
              quality results or to meet computational constraints. The S²KF
              contains the UKF with equally weighted samples as a special case
              when using the same amount of samples. With an increasing
              number of samples, the new filter converges to the (typically
              unfeasible) exact analytic statistical linearization. Hence, the
              S²KF can be seen as the ultimate generalization of all LRKFs
              such as the UKF, sigma-point filters, higher-order variants etc.,
              as it homogeneously covers the state space with a freely chosen
              number of samples. It is evaluated against state-of-the-art LRKFs
              by performing nonlinear prediction and extended target tracking.}
}

@article{JAIF14_Kurz-Bingham,
  author   = {Gerhard Kurz and Igor Gilitschenski and Simon Julier and Uwe D. Hanebeck},
  title    = {{Recursive Bingham Filter for Directional Estimation Involving 180 Degree Symmetry}},
  journal  = {Journal of Advances in Information Fusion},
  number   = {2},
  pages    = {90--105},
  volume   = {9},
  year     = {2014},
  month    = dec,
  pdf      = {JAIF13_Kurz-Bingham.pdf},
  abstract = {This work considers filtering of uncertain data
              defined on periodic domains, particularly the circle and the
              manifold of orientations in 3D space. Filters based on the Kalman
              filter perform poorly in this directional setting as they fail to take
              the structure of the underlying manifold into account. We present
              a recursive filter based on the Bingham distribution, which is
              defined on the considered domains. The proposed filter can be
              applied to circular filtering problems with 180 degree symmetry
              and to estimation of orientations in three dimensional space.
              It is easily implemented using standard numerical techniques
              and suitable for real-time applications. We evaluate our filter
              in a challenging scenario and compare it to a Kalman filtering
              approach adapted to the particular setting.}
}

@inproceedings{CDC14_Baum,
  author    = {Marcus Baum and Peter Willet and Uwe D. Hanebeck},
  title     = {{MMOSPA-based Track Extraction in the PHD Filter -- A Justification for k-Means Clustering}},
  booktitle = {Proceedings of the 53rd IEEE Conference on Decision and Control (CDC 2014)},
  year      = {2014},
  address   = {Los Angeles, California, USA},
  abstract  = {Displaying tracks is an essential part of a multi-
               target tracking system. Recently, it was proposed to extract
               tracks with respect to the Optimal Sub-Pattern Assignment
               (OSPA) metric, i.e., the traditionally used squared error loss
               is replaced with an OSPA loss, which leads to the so-called
               Minimum Mean OSPA (MMOSPA) estimate. So far, work
               concentrated on traditional trackers that maintain probability
               densities for the targets. In this paper, we aim at extracting
               the MMOSPA estimate from a Probability Hypothesis Density
               (PHD) as used within the PHD filter. We elaborate that the PHD
               in general does not contain enough information to determine
               the exact MMOSPA estimate. However, we then show that if
               the loss function has a specific form, it is indeed possible to
               extract point estimates from a PHD that are optimal w.r.t. the
               underlying unknown random finite set. We discuss two specific
               loss functions that fulfill this condition and are potentially close
               to the OSPA loss, a nearest neighbor loss and a kernel distance
               loss. It turns out that track extraction based on the nearest
               neighbor loss can be performed with the well-known k-means
               algorithm. Simulations show when the estimates based on the
               nearest neighbor and the kernel loss are close to the MMOSPA
               estimate.},
  pdf       = {CDC14_Baum.pdf},
  month     = dec
}

@inproceedings{CDC14_Dolgov,
  author    = {Maxim Dolgov and J\"{o}rg Fischer and Uwe D. Hanebeck},
  title     = {{Sequence-based LQG Control with Linear Integral Constraints over Stochastic Networks}},
  booktitle = {Proceedings of the 53rd IEEE Conference on Decision and Control (CDC 2014)},
  year      = {2014},
  address   = {Los Angeles, California, USA},
  abstract  = {In this paper, we consider sequence-based LQG control
               of stochastic linear systems with linear integral state and
               input constraints over networks subject to stochastic packet
               delays and losses. For this scenario, we derive a
               novel closed-loop optimal control law that consists of a
               feedback and a feedforward term. The feedback term depends
               linearly on the state estimate, while the feedforward term
               depends on the initial system state and the constraint functions.
               The control law can be partially given in closed-form that
               can be precomputed offline, and a numerical part which
               demands a solution of a quadratic optimization procedure.
               The number of the decision variables corresponds to the
               number of constraints. The presented control law is
               evaluated by means of a Monte-Carlo simulation.},
  pdf       = {CDC14_Dolgov.pdf},
  month     = dec
}

@inproceedings{CDC14_Kurz,
  author    = {Gerhard Kurz and Igor Gilitschenski and Maxim Dolgov and Uwe D. Hanebeck},
  title     = {{Bivariate Angular Estimation Under Consideration of Dependencies Using Directional Statistics}},
  booktitle = {Proceedings of the 53rd IEEE Conference on Decision and Control (CDC 2014)},
  year      = {2014},
  address   = {Los Angeles, California, USA},
  abstract  = {Estimation of angular quantities is a widespread
               issue, but standard approaches neglect the true topology of the
               problem and approximate directional with linear uncertainties.
               In recent years, novel approaches based on directional statistics
               have been proposed. However, these approaches have been
               unable to consider arbitrary circular correlations between
               multiple angles so far. For this reason, we propose a novel
               recursive filtering scheme that is capable of estimating multiple
               angles even if they are dependent, while correctly describing
               their circular correlation. The proposed approach is based on
               toroidal probability distributions and a circular correlation
               coefficient. We demonstrate the superiority to a standard
               approach based on the Kalman filter in simulations.},
  pdf       = {CDC14_Kurz.pdf},
  month     = dec
}

@inproceedings{SDF14_Faion,
  author    = {Florian Faion and Antonio Zea and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Bayesian Estimation of Line Segments}},
  booktitle = {{Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2014)}},
  year      = {2014},
  address   = {Bonn, Germany},
  month     = oct,
  pdf       = {SDF14_Faion.pdf},
  abstract  = {A popular approach when tracking extended objects
               with elongated shapes, such as ships or airplanes, is to
               approximate them as a line segment. Despite its simple shape,
               the distribution of measurement sources on a line segment can
               be characterized in many radically different ways. The spectrum
               ranges from Spatial Distribution Models that assume a distinct
               probability for each individual source, to Greedy Association
               Models as used in curve fitting, which do not assume any
               distribution at all. In between these border cases, Random
               Hypersurface Models assume a distribution over subsets of all
               sources. In this paper, we compare Bayesian estimators based
               on these different models. We point out their advantages and
               disadvantages and evaluate their performance by means of
               illustrative examples with synthetic and real data using a Linear
               Regression Kalman Filter.}
}

@inproceedings{SDF14_Gilitschenski,
  author    = {Igor Gilitschenski and Uwe D. Hanebeck},
  title     = {{A Direct Method for Checking Overlap of Two Hyperellipsoids}},
  booktitle = {{Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2014)}},
  year      = {2014},
  address   = {Bonn, Germany},
  month     = oct,
  pdf       = {SDF14_Gilitschenski.pdf},
  abstract  = {In this work, we propose a method for checking
               whether two arbitrary-dimensional hyperellipsoids overlap without
               making use of any optimization or root-finding methods.
               This is achieved by formulating an overlap condition as a
               polynomial root counting problem, which can be solved directly.
               The addressed challenges involve the inversion of a polynomial
               matrix using a direct method. The proposed approach extends one
               of our earlier results, which was restricted to certain combinations
               of ellipsoids and yields a fixed run-time for a fixed problem
               dimensionality. Thus, for the first time, an algorithm for checking
               overlap of arbitrary hyperellipsoids is proposed that can be
               evaluated in closed form. That is, in the absence of cut-off errors,
               the proposed method yields an exact result after a finite number
               of steps.}
}

@inproceedings{SDF14_Kurz,
  author    = {Gerhard Kurz and Igor Gilitschenski and Uwe D. Hanebeck},
  title     = {{Efficient Evaluation of the Probability Density Function of a Wrapped Normal Distribution}},
  booktitle = {{Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2014)}},
  year      = {2014},
  address   = {Bonn, Germany},
  abstract  = {The wrapped normal distribution arises when the
               density of a one-dimensional normal distribution is wrapped
               around the circle infinitely many times. At first look, evaluation
               of its probability density function appears tedious as an infinite
               series is involved. In this paper, we investigate the evaluation
               of two truncated series representations. As one representation
               performs well for small uncertainties, whereas the other performs
               well for large uncertainties, we show that in all cases a small
               number of summands is sufficient to achieve high accuracy.},
  pdf       = {SDF14_Kurz.pdf},
  month     = oct
}

@inproceedings{SDF14_Zea,
  author    = {Antonio Zea and Florian Faion and Uwe D. Hanebeck},
  booktitle = {{Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2014)}},
  title     = {{Tracking Extended Objects using Extrusion Random Hypersurface Models}},
  year      = {2014},
  address   = {Bonn, Germany},
  month     = oct,
  abstract  = {As sensor resolution increases, the accuracy and robustness
               of tracking algorithms can be improved by incorporating
               more information about the shape of the target object. This raises
               the need for simple and robust shape models capable of describing
               detailed objects. In this paper we propose an approach based on
               Random Hypersurface Models that interprets target shapes as
               scaled extrusions. This is achieved by combining projection-based
               models with probabilistic approaches, integrating the strengths
               of both mechanisms. As extruded shapes such as bottles, boxes,
               or containers can be extensively found in everyday situations,
               this approach can be applied for tracking in a large variety of
               environments.},
  annote    = {<b>Winner Best Paper Award</b> <a href="https://isas.iar.kit.edu/pdf/award/SDF14_best_paper_award.pdf" target="_blank">Certificate (PDF)</a>},
  pdf       = {SDF14_Zea.pdf}
}

@inproceedings{MFI14_Gilitschenski,
  author    = {Igor Gilitschenski and Gerhard Kurz and Simon J. Julier and Uwe D. Hanebeck},
  title     = {{Efficient Bingham Filtering based on Saddlepoint Approximations}},
  booktitle = {Proceedings of the 2014 IEEE International Conference on Multisensor Fusion and Information Integration (MFI 2014)},
  year      = {2014},
  address   = {Beijing, China},
  abstract  = {In this paper, we discuss computational efficiency
               of a recursive estimator using the Bingham Distribution. The
               Bingham distribution is defined directly on the unit hypersphere.
               As such, it is able to describe both large and small uncertainties
               in a unified framework. In order to tackle the challenging
               computation of the normalization constant, we propose a method
               using its Saddlepoint approximations and an approximate MLE
               based on the Gauss-Newton method. In a set of simulation
               experiments, we demonstrate that the Bingham filter not only
               outperforms both Kalman and particle filters, but can also be
               implemented efficiently.},
  pdf       = {MFI2014_Gilitschenski.pdf},
  month     = sep
}

@inproceedings{MFI14_Kurz,
  author    = {Gerhard Kurz and Igor Gilitschenski and Uwe D. Hanebeck},
  title     = {{The Partially Wrapped Normal Distribution for SE(2) Estimation}},
  booktitle = {Proceedings of the 2014 IEEE International Conference on Multisensor Fusion and Information Integration (MFI 2014)},
  year      = {2014},
  address   = {Beijing, China},
  abstract  = {We introduce a novel probability distribution on the
               group of rigid motions SE(2) and we refer to this distribution
               as the partially wrapped normal distribution. Describing probabilities
               on the SE(2) is of interest in a wide range of areas, for
               example robotics, autonomous vehicles, or information fusion. We
               derive some important properties of this novel distribution and
               derive an estimation scheme for its parameters based on moment
               matching. Furthermore, we provide a comparison to a recently
               published approach based on the Bingham distribution, and show
               that there are complementary advantages and disadvantages of
               the two approaches.},
  pdf       = {MFI2014_Kurz-PWN.pdf},
  month     = sep
}

@inproceedings{MFI14_Reinhardt,
  author    = {Marc Reinhardt and Sanjeev Kulkarni and Uwe D. Hanebeck},
  title     = {{Generalized Covariance Intersection based on Noise Decomposition}},
  booktitle = {Proceedings of the 2014 IEEE International Conference on Multisensor Fusion and Information Integration (MFI 2014)},
  year      = {2014},
  address   = {Beijing, China},
  abstract  = {In linear decentralized estimation, several nodes
               concurrently aim to recursively estimate the state of a common
               phenomenon by means of local measurements and data exchanges
               subject to limited knowledge. In this contribution, an efficient
               algorithm for consistent estimation in sensor networks under
               Kalman filter assumptions is derived. The main theorems generalize
               Covariance Intersection by means of an explicit consideration
               of individual noise terms. We apply the results to linear
               decentralized estimation and obtain covariance bounds with a
               scalable precision between the exact covariances and the bounds
               provided by Covariance Intersection subject to computation and
               communication effort.},
  pdf       = {MFI2014_Reinhardt.pdf},
  month     = sep
}

@inproceedings{CURAC14_Kurz,
  author    = {Gerhard Kurz and Genevi{\`{e}}ve Foley and P{\'{e}}ter Heged{\"{u}}s and G{\'{a}}bor Szab{\'{o}} and Uwe D. Hanebeck},
  title     = {{Evaluation of Image Stabilization Methods in Robotic Beating Heart Surgery}},
  booktitle = {13. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC14)},
  year      = {2014},
  address   = {Munich, Germany},
  month     = sep,
  abstract  = {In cardiopulmonary bypass surgery, it is beneficial to avoid the use of a heart-lung machine and perform beating heart
               surgery instead. This is a difficult task even for the most skilled surgeons. To eliminate the risks associated with cardio-
               pulmonary bypass on a beating heart, a motion compensation system can be used. We place markers on the heart surface,
               which we can use to track the complex heart motion and to produce still footage of the heart surface by applying one of
               several stabilization algorithms to eliminate the motion. We compare six different stabilization algorithms, affine, B-
               spline, piecewise linear and three types of radial basis functions. In this paper, we evaluate the results using three eval-
               uation methods, pixel intensity average difference, optical flow, and stabilized marker tracking. All of these show a sig-
               nificant reduction in motion after stabilization, especially for interpolation-based stabilization methods as opposed to the
               affine approximation. We discuss advantages and disadvantages of the different evaluation methods.},
  pdf       = {CURAC14_Kurz.pdf}
}

@inproceedings{IFAC14_Fischer,
  author    = {J{\"{o}}rg Fischer and Maxim Dolgov and Uwe D. Hanebeck},
  title     = {{Optimal Sequence-Based Tracking Control over Unreliable Networks}},
  booktitle = {Proceedings of the 19th IFAC World Congress (IFAC 2014)},
  year      = {2014},
  address   = {Cape Town, South Africa},
  abstract  = {In networked control systems, sequence-based controllers are used to compensate
               for transmission delays and losses in unreliable data networks. For this purpose, the controller
               sends not only the current control input to the actuator but also a sequence of predicted control
               inputs. The additional inputs can be used when subsequent transmissions get delayed or lost.
               In this paper, the sequence-based method is applied to the problem of trajectory tracking over
               an unreliable network and an optimal sequenced-based tracking controller is derived. The main
               advantage of the presented approach is that future information on the reference trajectory can
               optimally be embedded in the predicted control sequences. Furthermore, the controller can be
               implemented offline. An interesting result is that the optimal controller can still be separated
               into a feedback part and a feedforward part (as in standard optimal tracking control) despite of
               both the unreliable network and the sequence-based method. The performance of the derived
               tracking controller is demonstrated by Monte Carlo simulations with an inverted pendulum.},
  month     = aug,
  pdf       = {IFAC14_Fischer.pdf}
}

@inproceedings{IFAC14_Hanebeck,
  author    = {Uwe D. Hanebeck and Anders Lindquist},
  title     = {{Moment-based Dirac Mixture Approximation of Circular Densities}},
  booktitle = {Proceedings of the 19th IFAC World Congress (IFAC 2014)},
  year      = {2014},
  address   = {Cape Town, South Africa},
  abstract  = {Given a circular probability density function, called the true probability density
               function, the goal is to find a Dirac mixture approximation based on some circular moments of
               the true density. When keeping the locations of the Dirac points fixed, but almost arbitrarily
               located, we are applying recent results on the circulant rational covariance extension problem
               to the problem of calculating the weights. For the case of simultaneously calculating optimal
               locations, additional constraints have to be deduced from the given density. For that purpose,
               a distance measure for the deviation of the Dirac mixture approximation from the true density
               is derived, which then is minimized while considering the moment conditions as constraints.
               The method is based on progressive numerical minimization, converges quickly and gives well-
               distributed Dirac mixtures that fulfill the constraints, i.e., have the desired circular moments.},
  month     = aug,
  pdf       = {IFAC14_Hanebeck.pdf}
}

@inproceedings{IFAC14_Noack,
  author    = {Benjamin Noack and Joris Sijs and Uwe D. Hanebeck},
  title     = {{Fusion Strategies for Unequal State Vectors in Distributed Kalman Filtering}},
  booktitle = {Proceedings of the 19th IFAC World Congress (IFAC 2014)},
  year      = {2014},
  address   = {Cape Town, South Africa},
  abstract  = {Distributed implementations of state estimation algorithms generally have in
               common that each node in a networked system computes an estimate on the entire global
               state. Accordingly, each node has to store and compute an estimate of the same state vector
               irrespective of whether its sensors can only observe a small part of it. In particular, the task of
               monitoring large-scale phenomena renders such distributed estimation approaches impractical
               due to the sheer size of the corresponding state vector. In order to reduce the workload of the
               nodes, the state vector to be estimated is subdivided into smaller, possibly overlapping parts.
               In this situation, fusion does not only refer to the computation of an improved estimate but also
               to the task of reassembling an estimate for the entire state from the locally computed estimates
               of unequal state vectors. However, existing fusion methods require equal state representations
               and, hence, cannot be employed. For that reason, a fusion strategy for estimates of unequal
               and possibly overlapping state vectors is derived that minimizes the mean squared estimation
               error. For the situation of unknown cross-correlations between local estimation errors, also a
               conservative fusion strategy is proposed.},
  month     = aug,
  pdf       = {IFAC14_Noack.pdf}
}

@inproceedings{Fusion14_Ajgl,
  author    = {Ji{\v{r}}{\'{i}} Ajgl and Miroslav {\v{S}}imandl and Marc Reinhardt and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Covariance Intersection in State Estimation of Dynamical Systems}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {The Covariance Intersection algorithm linearly
               combines estimates when the cross-correlations between their
               errors are unknown. It provides a fused estimate and an upper
               bound of the corresponding mean square error matrix. The
               weights of the linear combination are designed in order to
               minimise the upper bound. This paper analyses the optimal
               weights in relation to state estimation of dynamical systems.
               It is shown that the use of the optimal upper bound in a
               standard recursive filtering does not lead to optimal upper bounds
               in subsequent processing steps. Unlike the fusion under full
               knowledge, the fusion under unknown cross-correlations can fuse
               the same information differently, depending on the independent
               information that will be available in the future.},
  pdf       = {Fusion14_Ajgl.pdf}
}

@inproceedings{Fusion14_Duan,
  author    = {Zhansheng Duan and Xiao-Rong Li and Uwe D. Hanebeck},
  title     = {{Multi-sensor Distributed Estimation Fusion Using Minimum Distance Sum}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {In multi-sensor distributed estimation fusion, local
               estimation errors are correlated in general. Two extreme ways to
               handle this correlation is either to ignore them completely or to
               have them fully considered. There is another case in the middle:
               it admits the existence of the correlation, but does not know how
               large it is. A sensible way is to set up an optimality criterion
               and optimize it over all possible such correlations. This work
               is a new development in the third class. First, a new general
               objective function is introduced, which is the minimum sum
               of statistical distances between the fused density and the local
               posterior densities. Then it is shown that the new criterion leads
               to a convex optimization problem if the Kullback-Leibler (KL)
               divergence is used as the statistical distance between assumed
               Gaussian densities. It is found that although the analytically
               obtained fused estimate using the new criterion differs from
               the simple convex combination rule only in mean squared error
               (MSE) by a scaling factor N (the number of sensors used), it is
               pessimistic semi-definite in MSE. Numerical examples illustrate
               the effectiveness of the proposed distributed fuser by comparing
               with several widely used distributed fusers.},
  pdf       = {Fusion14_Duan.pdf}
}

@inproceedings{Fusion14_Chlebek,
  author    = {Christof Chlebek and Uwe D. Hanebeck},
  title     = {{Pole-based Distance Measure for Change Detection in Linear Dynamic Systems}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {In this work, we derive a distance measure for
               the detection of changes in the behavior of linear dynamic
               single-input-single-output (SISO) systems based on input-output
               data. The distance is calculated as a function of the system
               poles, which are directly estimated from the given data. Poles
               represent a system as a set and have no identities, which is
               analogous to the nature of association-free multi-target tracking.
               This motivates the application of set distances known from
               multi-target tracking, namely the optimal subpattern assign-
               ment (OSPA) distance. Thus, the OSPA distance as well as a
               modification, the MAX-OSPA distance, are formulated as pole-
               distances between dynamic systems. In this formulation, the
               OSPA distance finds the optimal assingment by minimizing over
               the sum of distances between poles. The MAX-OSPA chooses
               an optimal assignment by minimizing the maximum distance
               between two poles. The proposed distances are evaluated in
               several simulations comparing the deterministic OSPA and MAX-
               OSPA to a state-of-the-art metric for autoregressive-moving-
               average (ARMA) processes, as well as OSPA and MAX-OSPA
               using the direct pole estimation and a two step-pole estimation
               utilizing recursive ARX (AutoRegressive model with eXogenous
               input) system identification.},
  pdf       = {Fusion14_Chlebek.pdf}
}

@inproceedings{Fusion14_Faion,
  author    = {Florian Faion and Antonio Zea and Uwe D. Hanebeck},
  title     = {{Reducing Bias in Bayesian Shape Estimation}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {This work considers the problem of estimating the
               parameters of an extended object based on noisy point observations
               from its boundary. The intention is to explore relationships
               between common approaches by breaking them down into their
               basic assumptions within the Bayesian framework. In doing so,
               we find that distance-minimizing curve fitting algorithms can be
               modeled by using a special Spatial Distribution Model, where
               the source distribution is approximated by a greedy one-to-one
               association of points to sources on the shape boundary. Based on
               this insight, we explore the origin of the estimation bias, which is
               a well-known issue of curve fitting algorithms. Furthermore, we
               derive a general scheme to alleviate its effect for arbitrary shapes,
               as well as for non-isotropic noise. This procedure is shown to be
               a generalization of related special solutions.},
  pdf       = {Fusion14_Faion.pdf}
}

@inproceedings{Fusion14_GilitschenskiKurz,
  author    = {Igor Gilitschenski and Gerhard Kurz and Simon J. Julier and Uwe D. Hanebeck},
  title     = {{A New Probability Distribution for Simultaneous Representation of Uncertain Position and Orientation}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {This work proposes a novel way to represent uncertainty
               on the Lie group of rigid-body motions in the plane. This is
               achieved by using dual quaternions for representation of a planar
               rigid-body motion and proposing a probability distribution from
               the exponential family of distributions that inherently respects the
               underlying structure of the representation. This is particularly
               beneficial in scenarios involving strong measurement noise. A
               relationship between the newly proposed distributional model
               and the Bingham distribution is discussed. The presented results
               involve formulas for computation of the normalization constant,
               the mode, parameter estimation techniques, and a closed-form
               Bayesian measurement fusion.},
  annote    = {<b>Best Student Paper Award First Runner-Up</b> <a href="https://isas.iar.kit.edu/pdf/award/Fusion2014_best_student_paper_award_first_runner_up.pdf" target="_blank">Certificate (PDF)</a>},
  pdf       = {Fusion14_GilitschenskiKurz.pdf}
}

@inproceedings{Fusion14_GilitschenskiSteinbring,
  author    = {Igor Gilitschenski and Jannik Steinbring and Uwe D. Hanebeck and Miroslav Simandl},
  title     = {{Deterministic Dirac Mixture Approximation of Gaussian Mixtures}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {In this work, we propose a novel way to approximating
               mixtures of Gaussian distributions by a set of deterministically
               chosen Dirac delta components. This approximation is performed
               by adapting a method for approximating single Gaussian
               distributions to the considered case. The proposed method turns
               the approximation problem into an optimization problem by
               minimizing a distance measure between the Gaussian mixture
               and its Dirac mixture approximation. Compared to the simple
               Gaussian case, the minimization criterion is much more complex
               as multiple, non-standard Gaussian distributions have to be considered.},
  pdf       = {Fusion14_GilitschenskiSteinbring.pdf}
}

@inproceedings{Fusion14_Hanebeck,
  author    = {Uwe D. Hanebeck},
  title     = {{Sample Set Design for Nonlinear Kalman Filters viewed as a Moment Problem}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {For designing sample sets for nonlinear Kalman
               filters, i.e., Linear Regression Kalman Filters (LRKFs), a new
               method is introduced for approximating Gaussian densities by
               discrete densities, so called Dirac Mixtures (DMs). This
               approximating DM should maintain the mean and some higher-
               order moments and should homogeneously cover the support
               of the original density. Homogeneous approximations require
               redundancy, which means there are more Dirac components than
               necessary for fulfilling the moment constraints. Hence, some
               sort of regularization is required as the solution is no longer
               unique. Two types of regularizers are possible: The first type
               ensures smooth approximations, e.g., in a maximum entropy
               sense. The second type we pursue here ensures closeness of
               the approximating density to the given Gaussian. As standard
               distance measures are typically not well defined for discrete
               densities on continuous domains, we focus on shifting the mass
               distribution of the approximating density as close to the true
               density as possible. Instead of globally comparing the masses as
               in a previous paper, the key idea is to characterize individual
               Dirac components by kernel functions representing the spread
               of probability mass that is appropriate at a given location. A
               distance measure is then obtained by comparing the deviation
               between the true density and the induced kernel density. As a
               result, the approximation problem is converted to an optimization
               problem as we now minimize the distance under the desired
               moment constraints.},
  pdf       = {Fusion14_Hanebeck.pdf}
}

@inproceedings{Fusion14_Sijs,
  author    = {Joris Sijs and Leon Kester and Benjamin Noack},
  title     = {{A Study on Event Triggering Criteria for Estimation}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {To reduce the amount of data transfer in networked
               systems measurements are usually taken only when an event
               occurs rather than periodically in time. However, a fundamental
               assessment on the response of estimation algorithms receiving
               event sampled measurements is not available. This research
               presents such an analysis when new measurements are sampled
               at well-designed events and sent to a Luenberger observer.
               Conditions are then derived under which the estimation error
               is bounded, followed by an assessment of two event sampling
               strategies when the estimator encounters two different types of
               disturbances: an impulse and a step disturbance. The sampling
               strategies are compared via four performance measures, such as
               estimation-error and communication resources. The result is a
               clear insight of the estimation response in an event-based setup.},
  pdf       = {Fusion14_Sijs.pdf}
}

@inproceedings{Fusion14_KurzGilitschenski,
  author    = {Gerhard Kurz and Igor Gilitschenski and Uwe D. Hanebeck},
  title     = {{Deterministic Approximation of Circular Densities with Symmetric Dirac Mixtures Based on Two Circular Moments}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {Circular estimation problems arise in many applications and can be
               addressed with the help of circular distributions. In particular, the
               wrapped normal and von Mises distributions are widely used in the
               context of circular problems. To facilitate the development of nonlinear
               filters, a deterministic sample-based approximation of these
               distributions with a so-called wrapped Dirac mixture distribution is
               beneficial. We propose a new closed-form solution to obtain a symmetric
               wrapped Dirac mixture with five components based on matching the first
               two circular moments. The proposed method is superior to
               state-of-the-art methods, which only use the first circular moment to
               obtain three Dirac components, because a larger number of Dirac
               components results in a more accurate approximation.},
  annote    = {<b>Winner Best Paper Award</b> <a href="https://isas.iar.kit.edu/pdf/award/Fusion2014_best_paper_award.pdf" target="_blank">Certificate (PDF)</a>},
  pdf       = {Fusion14_KurzGilitschenski.pdf}
}

@inproceedings{Fusion14_Kurz,
  author    = {Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{2D and 3D Image Stabilization for Robotic Beating Heart Surgery}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {Image stabilization is relevant for various industrial and medical
               applications. In particular, we consider the use of image stabilization
               in robotic beating heart surgery. A robot, which is remotely controlled
               by the surgeon, can automatically compensate for the motion of the
               beating heart. To give the surgeon the illusion of operating on a
               stationary heart, a stabilized image of the beating heart is shown to
               the surgeon. Image stabilization cancels the unwanted motion of the
               heart, but retains changes to color and texture, for example cuts on the
               heart surface. In this paper, stabilization is first considered as a 2D
               image transformation problem. Subsequently, it is extended to
               stabilization of a 3D point cloud or surface. The proposed algorithms
               are evaluated in both ex-vivo and in-vivo experiments. In the
               evaluation, the stabilization quality achievable with several common
               interpolation functions is compared.},
  pdf       = {Fusion14_Kurz.pdf}
}

@inproceedings{Fusion14_Noack,
  author    = {Benjamin Noack and Marc Reinhardt and Uwe D. Hanebeck},
  title     = {{On Nonlinear Track-to-track Fusion with Gaussian Mixtures}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {The problem of fusing state estimates is encountered in many network-based multi-sensor applications.
               The majority of distributed state estimation algorithms are designed to provide multiple estimates on the
               same state, and track-to-track fusion then refers to the task of combining these estimates. While linear
               fusion only requires the joint cross-covariance matrix to be known, dependencies between estimates in
               nonlinear estimation problems have to be represented by high-dimensional probability density functions.
               In general, storing and keeping track of nonlinear dependencies is too cumbersome. However, this paper
               demonstrates that estimates represented by Gaussian mixtures prove to be an important exception to this rule.
               The dependency structure can as well be characterized in terms of a higher-dimensional Gaussian mixture.
               The different processing steps of distributed nonlinear state estimation, i.e., prediction, filtering,
               and fusion, are studied in light of the joint density representation. The presented concept is complemented
               with different simpler suboptimal representations of the dependency structure between Gaussian mixture densities.},
  pdf       = {Fusion14_Noack.pdf}
}

@inproceedings{Fusion14_Reinhardt,
  author    = {Marc Reinhardt and Benjamin Noack and Sanjeev Kulkarni and Uwe D. Hanebeck},
  title     = {{Distributed Kalman Filtering in the Presence of Packet Delays and Losses}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {Distributed Kalman filtering aims at optimizing an
               estimate at a fusion center based on information that is gathered
               in a sensor network. Recently, an exact solution based on local
               estimation tracks has been proposed and an extension to cope
               with packet losses has been derived. In this contribution, we
               generalize both algorithms to packet delays. The key idea is
               to introduce augmented measurement vectors in the sensors
               that permit the optimization of local filter gains according to
               time-dependent measurement capabilities at the fusion center.
               In the most general form, the algorithm provides optimized
               estimates in sensor networks with packets delays and losses. The
               precision depends on the actual arrival patterns, and the results
               correspond to those of the centralized Kalman filter when specific
               assumptions about the measurement capability are satisfied.},
  pdf       = {Fusion14_Reinhardt.pdf}
}

@inproceedings{Fusion14_Steinbring,
  author    = {Jannik Steinbring and Uwe D. Hanebeck},
  title     = {{Progressive Gaussian Filtering Using Explicit Likelihoods}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {In this paper, we introduce a new sample-based
               Gaussian filter. In contrast to the popular Nonlinear Kalman
               Filters, e.g., the UKF, we do not rely on linearizing the
               measurement model. Instead, we take up the Gaussian progressive
               filtering approach introduced by the PGF 42 but explicitly rely
               on likelihood functions. Progression means, we incorporate the
               information of a new measurement gradually into the state
               estimate. The advantages of this filtering method are on the one
               hand the avoidance of sample degeneration and on the other
               hand an adaptive determination of the number of likelihood
               evaluations required for each measurement update. By this
               means, less informative measurements can be processed quickly,
               whereas measurements containing much information automatically
               receive more emphasis by the filter. These properties allow
               the new filter to cope with the demanding problem of very narrow
               likelihood functions in an efficient way.},
  pdf       = {Fusion14_Steinbring.pdf}
}

@inproceedings{Fusion14_Zea,
  author    = {Antonio Zea and Florian Faion and Uwe D. Hanebeck},
  title     = {{Tracking Connected Objects Using Interacting Shape Models}},
  booktitle = {Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)},
  year      = {2014},
  address   = {Salamanca, Spain},
  month     = jul,
  abstract  = {As sensor resolution increases, estimators tracking
               extended objects benefit from being able to closely model the
               shape of the target. However, as more shape details are
               incorporated, this usually leads to increasingly complex estimators.
               A more useful approach is to describe these shapes as a combination
               of simpler shapes connected to each other. In this paper, we
               propose a modular approach to estimate these combined targets
               in function of their simpler components. This allows the
               characteristics of each component to be encapsulated, and permits
               the combination of multiple filtering techniques as required by
               each component shape. This approach can be applied to track
               combined objects in a large variety of environments, such as
               excavators, robotic arms, wagon trains, and many others.},
  pdf       = {Fusion14_Zea.pdf}
}

@inproceedings{ECC14_Chlebek,
  author    = {Christof Chlebek and Uwe D. Hanebeck},
  title     = {{Bayesian Approach to Direct Pole Estimation}},
  booktitle = {Proceedings of the 2014 European Control Conference (ECC 2014)},
  year      = {2014},
  month     = jun,
  address   = {Strasbourg, France},
  abstract  = {In this work, a solution to the direct pole identification problem
               of discrete-time autoregressive (AR) processes by general recursive
               Bayesian estimation is presented. The poles of the transfer function
               of an AR process are identified directly from the process output
               data. Without intermediate estimation of the AR coefficient, the
               AR process identification problem by means of its poles becomes nonlinear,
               and thus cannot be solved exactly. A practical solution by application
               of statistical linearization is given. The derived direct pole estimation
               algorithm by statistical linearization is given in closed-form and
               regression point based, by the so-called Linear Regression Kalman
               Filter (LRKF). Two realizations of the LRKF algorithm are tested,
               namely the Unscented Kalman Filter (UKF) for low computational complexity
               and thus, for high update rates, and the Smart Sampling Kalman Filter
               (S2KF) for high precision with faster convergence. Both, the UKF
               and S2KF are compared to the Adaptive Pole Estimation (APE), a solution
               by recursive nonlinear least squares minimizing the prediction error
               gradient.},
  pdf       = {ECC14_Chlebek.pdf}
}

@inproceedings{SAM14_Baum,
  author    = {Marcus Baum and Peter Willett and Uwe D. Hanebeck},
  title     = {{MMOSPA-based Direction-of-Arrival Estimation for Planar Antenna Arrays}},
  booktitle = {Proceedings of the Eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2014)},
  year      = {2014},
  month     = jun,
  address   = {A Coru\~{n}a, Spain},
  abstract  = {This work is concerned with direction-of-arrival
               (DOA) estimation of narrowband signals from multiple targets
               using a planar antenna array. We illustrate the shortcomings of
               Maximum Likelihood (ML), Maximum a Posteriori (MAP), and
               Minimum Mean Squared Error (MMSE) estimation, issues that
               can be attributed to the symmetry in the likelihood function that
               must exist when there is no information about labeling of targets.
               We proffer the recently introduced concept of Minimum Mean
               OSPA (MMOSPA) estimation that is based on the optimal subpattern
               assignment (OSPA) metric for sets and hence inherently
               incorporates symmetric likelihood functions.},
  pdf       = {SAM14_Baum.pdf}
}

@inproceedings{SAM14_Kurz,
  author    = {Gerhard Kurz and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Real-time Kernel-based Multiple Target Tracking for Robotic Beating Heart Surgery}},
  booktitle = {Proceedings of the Eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2014)},
  year      = {2014},
  month     = jun,
  address   = {A Coru\~{n}a, Spain},
  abstract  = {Performing surgery on the beating heart has significant advantages for
               the patient compared to traditional heart surgery on the stopped heart.
               A remote-controlled robot can be used to automatically cancel out the
               movement of the beating heart. This necessitates precise tracking of the
               heart surface. For this purpose, we track 24 identical artificial
               markers placed on the heart. This creates a data association problem,
               because it is not known which measurement was obtained from which
               marker. To solve this problem, we apply a multiple target tracking
               method based on a symmetric kernel transformation. This method allows
               efficient handling of the data association problem even for a reasonably
               large number of targets. We demonstrate how to implement this method
               efficiently. The proposed approach is evaluated on in-vivo data of a
               real beating heart surgery performed on a porcine beating heart.},
  pdf       = {SAM14_Kurz.pdf}
}

@inproceedings{SAM14_Zea,
  author    = {Antonio Zea and Florian Faion and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Tracking Simplified Shapes Using a Stochastic Boundary}},
  booktitle = {Proceedings of the Eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2014)},
  year      = {2014},
  month     = jun,
  address   = {A Coru\~{n}a, Spain},
  abstract  = {When tracking extended objects, it is often the case that the shape of the
               target cannot be fully observed due to issues of visibility, artifacts, or
               high noise, which can change with time.
               In these situations, it is a common approach to model targets as simpler
               shapes instead, such as ellipsoids or cylinders.
               However, these simplifications cause information loss from the original
               shape, which could be used to improve the estimation results.
               In this paper, we propose a way to recover information from these lost
               details in the form of a stochastic boundary, whose parameters can be
               dynamically estimated from received measurements.
               The benefits of this approach are evaluated by tracking an object using
               noisy, real-life RGBD data.},
  pdf       = {SAM14_Zea.pdf}
}

@inproceedings{ACC14_Dolgov,
  author    = {Maxim Dolgov and Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{State Estimation for Stochastic Hybrid Systems Based on Deterministic Dirac Mixture Approximation}},
  booktitle = {Proceedings of the 2014 American Control Conference (ACC 2014)},
  year      = {2014},
  month     = jun,
  address   = {Portland, Oregon, USA},
  abstract  = {In this paper, we consider state estimation for Stochastic Hybrid
               Systems (SHS). These are systems that possess both continuous-valued and
               discrete-valued dynamics. For SHS with nonlinear hybrid dynamics and/or
               non-Gaussian disturbances, state estimation can be implemented as an
               Interacting Multiple Model (IMM) particle filter. However, a
               disadvantage of particle filtering is the computational load caused by
               the large number of particles required for a sufficiently good
               estimation. We address this issue by first expressing the probability
               density that describes the state of the SHS as a collection of densities
               of the continuous-valued state only conditioned on the discrete-valued
               state. Then, we deterministically approximate these individual densities
               with Dirac mixtures. The employed approximation method places the
               particles so that a so called modified Cram\'{e}r-von Mises distance
               between the true and the approximated density is minimized.
               Deterministic approximation requires far less particles than the
               stochastic sampling used by particle filters. To avoid particle
               degeneration that can occur when a density is multiplied with the
               likelihood, the filter uses progressive density correction. The
               presented filter is demonstrated in a numerical maneuvering target
               tracking example.},
  pdf       = {ACC14_Dolgov.pdf}
}

@inproceedings{ACC14_Kurz,
  author    = {Gerhard Kurz and Igor Gilitschenski and Uwe D. Hanebeck},
  title     = {{N}onlinear {M}easurement {U}pdate for {E}stimation of {A}ngular {S}ystems {B}ased on {C}ircular {D}istributions},
  booktitle = {Proceedings of the 2014 American Control Conference (ACC 2014)},
  year      = {2014},
  month     = jun,
  address   = {Portland, Oregon, USA},
  abstract  = {In this paper, we propose a novel progressive nonlinear measurement
               update for circular states. This generalizes our previously published
               circular filter that so far was limited to identity measurement
               equations. The new update method is based on circular distributions in
               order to capture the periodic properties of a circular system better
               than conventional approaches that rely on standard Gaussian
               distributions. Besides the progressive measurement update, we propose
               two additional measurement updates that are obtained by adapting
               traditional filters to the circular case. Simulations show the
               superiority of the proposed progressive approach.},
  pdf       = {ACC14_Kurz.pdf}
}

@article{JAIF14_Kurz,
  title    = {{D}ynamic {S}urface {R}econstruction by {R}ecursive {F}usion of {D}epth and {P}osition {M}easurements},
  author   = {Gerhard Kurz and Uwe D. Hanebeck},
  journal  = {Journal of Advances in Information Fusion},
  year     = {2014},
  month    = jun,
  url      = {https://confcats_isif.s3.amazonaws.com/web-files/journals/entries/427_1_art_6_19271\%5B1\%5D.pdf},
  number   = {1},
  pages    = {13--26},
  volume   = {9},
  abstract = {Surface estimation can be performed based on position or depth measurements.
              We propose a method to fuse both types of measurements. Position measurements are obtained
              from landmarks on the surface, i.e., they are fixed to a certain point on the surface. In
              contrast, depth measurements reflect the depth measured along a line emanating from a depth
              camera and are not fixed to a position on the surface. The proposed approach uses a mixture
              of Cartesian and polar or spherical coordinate to treat both measurement types accordingly.
              By doing so, the uncertainties associated with the different measurement types are explicitly
              considered. The presented method represents the surface by a spline and is applicable to both
              2D and 3D applications. Surface estimation is considered as a recursive filtering problem and
              standard nonlinear filtering methods suchas the unscented Kalman filter can be used to obtain
              surface estimates. We show a thorough evaluation of the proposed approach in simulations.},
  pdf      = {JAIF14_Kurz.pdf}
}

@inproceedings{SPIE14_Baum,
  author    = {Marcus Baum and Peter Willet and Yaakov Bar-Shalom and Uwe D. Hanebeck},
  title     = {{Approximate Calculation of Marginal Association Probabilities using a Hybrid Data Association Model}},
  booktitle = {SPIE -- Signal and Data Processing of Small Targets 2014},
  address   = {Baltimore, Maryland, USA},
  month     = jun,
  year      = {2014},
  doi       = {10.1117/12.2053431},
  url       = {https://dx.doi.org/10.1117/12.2053431},
  abstract  = {The calculation of marginal association probabilities is
               the major computational bottleneck in the Joint Probabilistic
               Data Association Filter (JPDAF). In this paper, we investigate
               approximations for the marginal associations that simplify the
               (computational complex) original association model in order to
               obtain efficient algorithms. In this context, we first discuss
               the Bakhtiar-Alavi algorithm and the Linear Multitarget Integrated
               Probabilistic Data Association (LMIPDA) algorithm. Second, we
               propose a fast novel approximation that exploits systematic
               combinations of the JPDAF measurement model with the
               Probabilistic Multi-Hypothesis Tracker (PMHT) measurement model.
               The discussed methods are evaluated by means of a tracking
               scenario with a high number of closely-spaced targets.}
}

@inproceedings{CISS14_Reinhardt,
  author    = {Marc Reinhardt and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Reconstruction of Joint Covariance Matrices in Networked Linear Systems}},
  year      = {2014},
  abstract  = {In this paper, a sample representation of the estimation error is
               utilized to reconstruct the joint covariance matrix in a distributed estimation
               system. The key idea is to sample uncorrelated and fully correlated noise
               according to different techniques at local estimators without knowledge about
               the processing of other nodes in the network. This way, the correlation between
               estimates is inherently linked to the representation of the corresponding
               sample sets. We discuss the noise processing, derive key attributes, and
               evaluate the precision of the covariance estimates.},
  booktitle = {Proceedings of the 48th Annual Conference on Information Sciences and Systems (CISS 2014)},
  address   = {Princeton, New Jersey, USA},
  month     = mar,
  pdf       = {CISS14_Reinhardt.pdf}
}

@inproceedings{CISS14_Hanebeck,
  author    = {Uwe D. Hanebeck},
  title     = {{Kernel-based Deterministic Blue-noise Sampling of Arbitrary Probability Density Functions}},
  year      = {2014},
  abstract  = {This paper provides an efficient method for approximating a given
               continuous probability density function (pdf) by a Dirac mixture density. Optimal
               parameters are determined by systematically minimizing a distance measure. As
               standard distance measures are typically not well defined for discrete densities
               on continuous domains, we focus on shifting the mass distribution of the
               approximating density as close to the true density as possible. Instead of globally
               comparing the masses as in a previous paper, the key idea is to characterize
               individual Dirac components by kernel functions representing the spread of
               probability mass that is appropriate at a given location. A distance measure is
               then obtained by comparing the deviation between the true density and the induced
               kernel density. This new method for Dirac mixture approximation provides
               high-quality approximation results, can handle arbitrary pdfs, allows considering
               constraints for, e.g., maintaining certain moments, and is fast enough for online
               processing.},
  booktitle = {Proceedings of the 48th Annual Conference on Information Sciences and Systems (CISS 2014)},
  address   = {Princeton, New Jersey, USA},
  month     = mar,
  pdf       = {CISS14_Hanebeck.pdf}
}

@incollection{SPP14_Fischer,
  author    = {J{\"{o}}rg Fischer and Uwe D. Hanebeck},
  title     = {{D}istributed and {N}etworked {M}odel {P}redictive {C}ontrol},
  booktitle = {{Control Theory of Digitally Networked Dynamic Systems}},
  publisher = {Springer International Publishing},
  year      = {2014},
  month     = jan,
  editor    = {Jan Lunze},
  pages     = {111--167},
  doi       = {10.1007/978-3-319-01131-8_4},
  isbn      = {978-3-319-01130-1},
  url       = {https://dx.doi.org/10.1007/978-3-319-01131-8_4},
  abstract  = {In this chapter, we consider the problem of controlling networked
               and distributed systems by means of model predictive control (MPC).
               The basic idea behind MPC is to repeatedly solve an optimal control
               problem based on a model of the system to be controlled. Every time
               a new measurement is available, the optimization problem is solved
               and the corresponding input sequence is applied until a new measurement
               arrives. As explained in the sequel, the advantages of MPC over other
               control strategies for networked systems are due to the fact that
               a model of the system is available at the controller side, which
               can be used to compensate for random bounded delays. At the same
               time, for each iteration of the optimization problem an optimal input
               sequence is calculated. In case of packet dropouts, one can reuse
               this information to maintain closed-loop stability and performance.},
  pdf       = {SPP14_Fischer.pdf}
}

@inproceedings{CDC13_Fischer,
  author    = {J{\"{o}}rg Fischer and Maxim Dolgov and Uwe D. Hanebeck},
  title     = {{On Stability of Sequence-Based LQG Control}},
  booktitle = {Proceedings of the 52st IEEE Conference on Decision and Control (CDC 2013)},
  year      = {2013},
  address   = {Florence, Italy},
  abstract  = {Sequence-based control is a well-established method applied in
               Networked Control Systems (NCS) to mitigate the effect of time-varying
               transmission delays and stochastic packet losses. The idea of this method
               is that the controller sends sequences of predicted control inputs to the
               actuator that can be applied in case a future transmission fails. In this paper,
               the stability properties of sequence-based LQG controllers are analyzed
               in terms of the boundedness of the long run average costs. On the one hand,
               we derive sufficient conditions, each for the boundedness and unboundedness
               of the costs. On the other hand, we give bounds on the minimal length of
               the control input sequence needed to stabilize a system.},
  pdf       = {CDC13_Fischer.pdf},
  month     = dec
}

@inproceedings{CURAC13_Kurz,
  author    = {Gerhard Kurz and Peter Hegedus and G{\'{a}}bor Szab{\'{o}} and Uwe D. Hanebeck},
  title     = {{Experimental Evaluation of Kinect and Inertial Sensors for Beating Heart Tracking}},
  booktitle = {12. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC13)},
  year      = {2013},
  address   = {Innsbruck, Austria},
  month     = nov,
  abstract  = {This paper investigates the use of Kinect depth sensors as well as
               inertial sensors in the context of beating heart surgery. In the past,
               various sensors have been used in attempts to track the beating heart,
               each with its own distinct set of advantages and disadvantages. With the
               availability of affordable structured-light depth sensors such as the
               Kinect and sufficiently small and low priced inertial sensors, the
               question of their suitability for beating heart tracking arises. We
               performed in-vivo experiments on a porcine heart in order to assess the
               feasibility of beating heart tracking based on these sensors.},
  pdf       = {CURAC13_Kurz.pdf}
}

@inproceedings{IPIN13_Kurz,
  author    = {Gerhard Kurz and Florian Faion and Uwe D. Hanebeck},
  title     = {{Constrained Object Tracking on Compact One-dimensional Manifolds Based on Directional Statistics}},
  booktitle = {Proceedings of the Fourth IEEE GRSS International Conference on Indoor Positioning and Indoor Navigation (IPIN 2013)},
  year      = {2013},
  address   = {Montbeliard, France},
  abstract  = {In this paper, we present a novel approach for tracking objects whose
               movement is constrained to a compact one-dimensional manifold, for
               example a conveyer belt or a mobile robot whose movement is restricted
               to tracks. Standard approaches either ignore the constraint at first and
               retroactively move the estimate to lie on the manifold, or consider the
               tracking problem on a manifold but falsely assume a Gaussian
               distribution. Our method explicitly takes the actual topology into
               account from the beginning and relies on special types of probability
               distributions defined on the proper manifold. In particular, we consider
               objects moving along a closed one-dimensional track, for example an
               ellipse, a polygon, or similar closed shapes. This shape is transformed
               to a circle with a homeomorphism. Thus, we can apply a recursive
               circular filtering algorithm to the constrained tracking problem.
               Finally, the estimate is transformed back to the original manifold. We
               evaluate the proposed method in an experiment by tracking a toy train
               moving along a track and comparing the results to those of traditional
               approaches for this problem.},
  pdf       = {IPIN13_Kurz.pdf},
  month     = oct
}

@inproceedings{NecSys13_Dolgov,
  author    = {Maxim Dolgov and J{\"{o}}rg Fischer and Uwe D. Hanebeck},
  title     = {{Event-based LQG Control over Networks Subject to Random Transmission Delays and Packet Losses}},
  booktitle = {Proceedings of the 4th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys 2013)},
  year      = {2013},
  address   = {Koblenz, Germany},
  abstract  = {In Networked Control Systems (NCS), data networks not only
               limit the amount of information exchanged by system components but
               are also subject to stochastic packet delays and losses. In this
               paper, we present a controller that simultaneously addresses these
               problems by combining event-based and sequence-based control methods.
               At every time step, the proposed controller calculates a sequence
               of predicted control inputs and based on the expected future LQG
               costs decides whether it transmits the control sequence to the actuator.
               The proposed controller is evaluated with simulations.},
  pdf       = {NecSys13_Dolgov.pdf},
  month     = sep
}

@inproceedings{ECC13_Sijs,
  author    = {Joris Sijs and Uwe D. Hanebeck and Benjamin Noack},
  title     = {{An Empirical Method to Fuse Partially Overlapping State Vectors for Distributed State Estimation}},
  booktitle = {Proceedings of the 2013 European Control Conference (ECC 2013)},
  year      = {2013},
  address   = {Z{\"{u}}rich, Switzerland},
  abstract  = {State fusion is a method for merging multiple estimates of the same
               state into a single fused estimate. Dealing with multiple estimates
               is one of the main concerns in distributed state estimation, where
               an estimated value of the desired state vector is computed in each
               node of a networked system. Most solutions for distributed state
               estimation currently available assume that every node computes an
               estimate of the (same) global state vector. This assumption is impractical
               for systems observing large-area processes, due to the sheer size
               of the process state. A more feasible solutions is one where each
               node estimates a part of the global state vector, allowing different
               nodes in the network to have overlapping state elements. Although
               such an approach should be accompanied by a corresponding state fusion
               method, existing solutions cannot be employed as they merely consider
               fusion of two different estimates with equal state representations.
               Therefore, an empirical solution is presented for fusing two state
               estimates that have partially overlapping state elements. A justification
               of the proposed fusion method is presented, along with an illustrative
               case study for observing the temperature profile of a large rod,
               though a formal derivation is future research.},
  pdf       = {ECC13_Sijs.pdf},
  month     = jul
}

@inproceedings{Fusion13_Baum,
  author    = {Marcus Baum and Uwe D. Hanebeck},
  title     = {{The Kernel-SME Filter for Multiple Target Tracking}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {We present a novel method for tracking multiple targets, called Kernel-SME
               filter, that does not require an enumeration of measurement-to-target
               associations. This method is a further development of the symmetric
               measurement equation (SME) filter that removes the data association
               uncertainty of the original measurement equation with the help of
               a symmetric transformation. The key idea of the Kernel-SME filter
               is to define a symmetric transformation that maps the measurements
               to a Gaussian mixture function. This transformation is scalable to
               a large number of targets and allows for deriving a Gaussian state
               estimator that only has a cubic runtime complexity in the number
               of targets.},
  pdf       = {Fusion13_Baum.pdf},
  month     = jul
}

@inproceedings{Fusion13_Faion,
  author    = {Florian Faion and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Silhouette Measurements for Bayesian Object Tracking in Noisy Point Clouds}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {In this paper, we consider the problem of jointly tracking the pose
               and shape of objects based on noisy data from cameras and depth sensors.
               Our proposed approach

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