text:
ear regression Kalman filter [1, 2]. A decomposed com-
               putation of the filter step is introduced, allowing a more
               efficient calculation.},
  pdf       = {Fusion10_DunauPacki.pdf},
  month     = jul
}

@inproceedings{Fusion10_EberhardtKlumpp,
  author    = {Henning Eberhardt and Vesa Klumpp and Uwe D. Hanebeck},
  booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
  title     = {{Density Trees for Efficient Nonlinear State Estimation}},
  year      = {2010},
  address   = {Edinburgh, United Kingdom},
  month     = jul,
  abstract  = {In this paper, a new class of nonlinear Bayesian
               estimators based on a special space partitioning structure, generalized
               Octrees, is presented. This structure minimizes memory and calculation
               overhead. It is used as a container framework for a set of node functions
               that approximate a density piecewise. All necessary operations are derived
               in a very general way in order to allow for a great variety of Bayesian
               estimators. The presented estimators are especially well suited for
               multi-modal nonlinear estimation problems. The running time performance
               of the resulting estimators is first analyzed theoretically and then backed
               by means of simulations. All operations have a linear running time in
               the number of tree nodes.},
  pdf       = {Fusion10_EberhardtKlumpp.pdf}
}

@inproceedings{Fusion10_Klumpp-SGMF,
  author    = {Vesa Klumpp and Frederik Beutler and Uwe D. Hanebeck and Dietrich Fr{\"a}nken},
  title     = {{The Sliced Gaussian Mixture Filter with Adaptive State Decomposition Depending on Linearization Error}},
  booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
  address   = {Edinburgh, United Kingdom},
  year      = {2010},
  abstract  = {In this paper, a novel nonlinear/non-linear model
               decomposition for the Sliced Gaussian Mixture Filter is presented.
               Based on the level of nonlinearity of the model, the overall estimation
               problem is decomposed into a severely nonlinear and a slightly
               nonlinear part, which are processed by different estimation techniques.
               To further improve the efficiency of the estimator, an adaptive state
               decomposition algorithm is introduced that allows decomposition
               according to the linearization error for nonlinear system and
               measurement models. Simulations show that this approach has orders of
               magnitude less complexity compared to other state of the art
               estimators, while maintaining comparable estimation errors.},
  pdf       = {Fusion10_Klumpp-SGMF.pdf},
  month     = jul
}

@inproceedings{Fusion10_Klumpp,
  author    = {Vesa Klumpp and Benjamin Noack and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Combined Set-Theoretic and Stochastic Estimation: A Comparison of the SSI and the CS Filter}},
  booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
  address   = {Edinburgh, United Kingdom},
  year      = {2010},
  abstract  = {In estimation theory, mainly set-theoretic or
               stochastic uncertainty is considered. In some cases, especially when
               some statistics of a distribution are not known or additional
               stochastic information is used in a set-theoretic estimator, both
               types of uncertainty have to be considered. In this paper, two
               estimators that cope with combined stoachastic and set-theoretic
               uncertainty are compared, namely the Set-theoretic and Statistical
               Information filter, which represents the uncertainty by means of
               random sets, and the Credal State filter, in which the state
               information is given by sets of probability density functions.
               The different uncertainty assessment in both estimators leads to
               different estimation results, even when the prior information and
               the measurement and system models are equal. This paper explains
               these differences and states directions, when which estimator
               should be applied to a given estimation problem.},
  pdf       = {Fusion10_Klumpp.pdf},
  month     = jul
}

@inproceedings{Fusion10_KrauthausenHuber,
  author    = {Peter Krauthausen and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Support-Vector Conditional Density Estimation for Nonlinear Filtering}},
  booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
  address   = {Edinburgh, United Kingdom},
  year      = {2010},
  pdf       = {Fusion10_KrauthausenHuber.pdf},
  abstract  = {A non-parametric conditional density
               estimation algorithm for nonlinear stochastic dynamic
               systems is proposed. The contributions are a novel sup-
               port vector regression for estimating conditional den-
               sities, modeled by Gaussian mixture densities, and an
               algorithm based on cross-validation for automatically
               determining hyper-parameters for the regression. The
               conditional densities are employed with a modi?ed axis-
               aligned Gaussian mixture filter. The experimental va-
               lidation shows the high quality of the conditional densi-
               ties and good accuracy of the proposed filter.},
  month     = jul
}

@inproceedings{Fusion10_Noack,
  author    = {Benjamin Noack and Vesa Klumpp and Nikolay Petkov and Uwe D. Hanebeck},
  booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
  title     = {{Bounding Linearization Errors with Sets of Densities in Approximate Kalman Filtering}},
  year      = {2010},
  address   = {Edinburgh, United Kingdom},
  month     = jul,
  abstract  = {Applying the Kalman filtering scheme to linearized system dynamics and observation models does in general not yield optimal state estimates.
               More precisely, inconsistent state estimates and covariance matrices are caused by neglected linearization errors.
               This paper introduces a concept for systematically predicting and updating bounds for the linearization errors within the Kalman filtering framework.
               To achieve this, an uncertain quantity is not characterized by a single probability density anymore, but rather by a set of densities and accordingly,
               the linear estimation framework is generalized in order to process sets of probability densities. By means of this generalization,
               the Kalman filter may then not only be applied to stochastic quantities, but also to unknown but bounded quantities.
               In order to improve the reliability of Kalman filtering results, the last-mentioned quantities are utilized to bound the typically neglected nonlinear parts of a linearized mapping.},
  pdf       = {Fusion10_Noack.pdf}
}

@inproceedings{SUTC10_Lyons,
  author    = {Daniel Lyons and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{A Log-Ratio Information Measure for Stochastic Sensor Management}},
  booktitle = {{Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC 2010)}},
  year      = {2010},
  address   = {Newport Beach, California, USA},
  month     = jun,
  pdf       = {SUTC10_Lyons.pdf},
  abstract  = {In distributed sensor networks, computational and energy resources are
               in general limited.  Therefore, an intelligent selection of sensors for
               measurements is of great importance to ensure both high estimation
               quality and an extended lifetime of the network. Methods from the theory
               of model predictive control together with information theoretic measures
               have been employed to pick sensors yielding measurements with high
               information value. We present a novel information measure that originates from a
               scalar product on a class of continuous probability densities and apply it
               to the field of sensor management. Aside from its mathematical justifications
               for quantifying the information content of probability densities, the most
               remarkable property of the measure, an analogon of the triangle inequality
               under Bayesian information fusion, is deduced. This allows for deriving
               computationally cheap upper bounds for the model predictive sensor selection
               algorithm and for comparing the performance of planning over different lengths of time horizons.}
}

@inproceedings{ACC10_Eberhardt,
  author    = {Henning Eberhardt and Vesa Klumpp and Uwe D. Hanebeck},
  title     = {{Optimal Dirac Approximation by Exploiting Independencies}},
  booktitle = {{Proceedings of the 2010 American Control Conference (ACC 2010)}},
  year      = {2010},
  address   = {Baltimore, Maryland, USA},
  month     = jun,
  keywords  = {Stochastic systems, Filtering},
  abstract  = {The sample-based recursive prediction of discrete-time nonlinear
               stochastic dynamic systems requires a regular reapproximation of the Dirac mixture
               densities characterizing the state estimate with an exponentially increasing number
               of components. For that purpose, a systematic approximation method is proposed that
               is deterministic and guaranteed to minimize a new type distance measure, the so
               called modified Cram\'{e}r-von Mises distance. A huge increase in approximation
               performance is achieved by exploiting structural independencies usually occurring
               between the random variables used as input to the system. The corresponding prediction
               step achieves optimal performance when no further assumptions can be made about the
               system function. In addition, the proposed approach shows a much better convergence
               compared to the prediction step of the particle filter and by far fewer Dirac components
               are required for achieving a given approximation quality. As a result, the new
               approximation method opens the way for the development of new fully deterministic and
               optimal stochastic state estimators for nonlinear dynamic systems.},
  pdf       = {ACC10_EberhardtKlumpp.pdf}
}

@inproceedings{ACC10_Krauthausen,
  author    = {Peter Krauthausen and Uwe D. Hanebeck},
  title     = {{Parameter Learning for Hybrid Bayesian Networks With Gaussian Mixture and Dirac Mixture Conditional Densities}},
  booktitle = {{Proceedings of the 2010 American Control Conference (ACC 2010)}},
  year      = {2010},
  address   = {Baltimore, Maryland, USA},
  month     = jun,
  abstract  = {In this paper, the first algorithm for learning hybrid Bayesian
               Networks with Gaussian mixture and Dirac mixture conditional densities from data
               given their structure is presented. The mixture densities to be learned allow for
               nonlinear dependencies between the variables and exact closedform inference. For
               learning the network's parameters, an incremental gradient ascent algorithm is derived.
               Analytic expressions for the partial derivatives and their combination with messages are
               presented. This hybrid approach subsumes the existing approach for purely discrete-valued
               networks and is applicable to partially observable networks, too. Its practicability is
               demonstrated by a reference example.},
  pdf       = {ACC10_Krauthausen.pdf}
}

@incollection{VMS10_Lyons,
  author    = {Daniel Lyons and Achim Hekler and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Ma{\ss}e f{\"u}r Wahrscheinlichkeitsdichten in der informationstheoretischen Sensoreinsatzplanung}},
  booktitle = {Verteilte Messsysteme},
  publisher = {KIT Scientific Publishing},
  year      = {2010},
  editor    = {Fernando {Puente Le{\'{o}}n} and Klaus-Dieter Sommer and Michael Heizmann},
  pages     = {121--132},
  month     = mar,
  abstract  = {Bei der Beobachtung eines r{\"a}umlich verteilten Ph{\"a}nomens mit einer
               Vielzahl von Sensoren ist die intelligente Auswahl von Messkonfigurationen aufgrund von
               beschr{\"a}nkten Rechen- und Kommunikationskapazit{\"a}ten entscheidend f{\"u}r die
               Lebensdauer des Sensornetzes. Mit der Sensoreinsatzplanung kann die im n{\"a}chsten
               Zeitschritt anzusteuernde Messkonfiguration dynamisch mittels einer stochastischen
               modell-pr{\"a}diktiven Planung {\"u}ber einen endlichen Zeithorizont bestimmt werden.
               Dabei wird als G{\"u}tekriterium die Maximierung des zu erwartenden Informationsgewinns
               durch zuk{\"u}nftige Messungen unter sparsamer Verwendung der Energieressourcen gew{\"a}hlt.
               In diesem Artikel wird ein neues Ma{\ss} f{\"u}r kontinuierliche Wahrscheinlichkeitsdichten
               vorgestellt, das sich kanonisch aus der Konstruktion eines Vektorraums f{\"u}r
               Wahrscheinlichkeitsdichten ergibt. Dieses Ma{\ss} wird als G{\"u}tefunktion in der
               vorausschauenden Sensoreinsatzplanung zur Bewertung des informationstheoretischen Einflu{\ss}
               von Messungen auf die aktuelle Zustandssch{\"a}tzung verwendet.},
  url       = {https://publikationen.bibliothek.kit.edu/1000015670}
}

@incollection{VMS10_Noack,
  author    = {Benjamin Noack and Vesa Klumpp and Daniel Lyons and Uwe D. Hanebeck},
  title     = {{S}ystematische {B}eschreibung von {U}nsicherheiten in der {I}nformationsfusion mit {M}engen von {W}ahrscheinlichkeitsdichten},
  booktitle = {Verteilte Messsysteme},
  publisher = {KIT Scientific Publishing},
  year      = {2010},
  editor    = {Fernando {Puente Le{\'{o}}n} and Klaus-Dieter Sommer and Michael Heizmann},
  pages     = {167--178},
  month     = mar,
  abstract  = {Die systematische Behandlung von Unsicherheiten stellt eine wesentliche
               Herausforderung in der Informationsfusion dar. Einerseits m{\"u}ssen geeignete Darstellungsformen
               f{\"u}r die Unsicherheiten bestimmt werden und andererseits darauf aufbauend effiziente
               Sch{\"a}tzverfahren hergeleitet werden. Im Allgemeinen wird zwischen stochastischen und
               mengenbasierten Unsicherheitsbeschreibungen unterschieden. Dieser Beitrag stellt ein Verfahren
               zur Zustandssch{\"a}tzung vor, welches simultan stochastische und mengenbasierte Fehlergr{\"o}{\ss}en
               ber{\"u}cksichtigen kann, indem unsichere Gr{\"o}{\ss}en nicht mehr durch eine einzelne
               Wahrscheinlichkeitsdichte, sondern durch eine Menge von Dichten repr{\"a}sentiert werden.
               Besonderes Augenmerk liegt hier auf den Vorteilen und Anwendungsm{\"o}glichkeiten dieser
               Unsicherheitsbeschreibung.},
  url       = {https://publikationen.bibliothek.kit.edu/1000015670}
}

@incollection{VMS10_Kuwertz,
  author    = {Achim Kuwertz and Marco F. Huber and Felix Sawo and Uwe D. Hanebeck},
  title     = {{Sensoreinsatzplanung zur Verfolgung von Quellen r{\"a}umlich ausgedehnter Ph{\"a}nomene}},
  booktitle = {Verteilte Messsysteme},
  publisher = {KIT Scientific Publishing},
  year      = {2010},
  editor    = {Fernando {Puente Le{\'{o}}n} and Klaus-Dieter Sommer and Michael Heizmann},
  pages     = {179--191},
  month     = mar,
  abstract  = {R{\"a}umlich ausgedehnte Ph{\"a}nomene wie Schadstoffverteilungen in Gew{\"a}ssern
               oder Temperaturverteilungen in R{\"a}umen werden vielfach durch unbekannte, aber gegebenenfalls
               sich bewegende Quellen erzeugt. Allerdings stehen in vielen praktisch relevanten Aufgabenstellungen
               Informationen zur Lokalisierung einer derartigen Quelle nur indirekt durch eine Vermessung des
               induzierten Ph{\"a}nomens zur Verf{\"u}gung, welche den Einsatz eines verteilten Messsystems erfordert.
               Die Messungen k{\"o}nnen dabei beispielsweise von einem station{\"a}ren Sensornetz oder von mobilen
               Sensoren stammen. In diesem Beitrag werden modellbasierte Verfahren zu echtzeitf{\"a}higen Lokalisierung
               und schritthaltenden Verfolgung von Quellen vorgestellt, welche gezielt r{\"a}umlich und zeitlich
               verteilte Messungen einsetzen. Um den Informationsgewinn und somit den Nutzen verteilter Messungen zu
               maximieren, spielt bei diesem Verfahren neben einer mathematischen Modellierung auch eine vorausschauende
               Sensoreinsatzplanung eine zentrale Rolle. Das in diesem Beitrag vorgeschlagene Planungsverfahren
               erm{\"o}glicht dabei die effiziente und ressourcenschonende Verfolgung beweglicher Quellen bei gleichzeitig
               hoher Lokalisierungsgenauigkeit.},
  url       = {https://publikationen.bibliothek.kit.edu/1000015670}
}

@inproceedings{PED10_Perez,
  author    = {Antonia P\'{e}rez Arias and Tobias Kretz and Peter Ehrhardt and Stefan Hengst and Peter Vortisch and Uwe D. Hanebeck},
  title     = {{Extended Range Telepresence for Evacuation Training in Pedestrian Simulations}},
  booktitle = {{Proccedings of the 5th International Conference on Pedestrian and Evacuation Dynamics (PED 2010), Springer-Verlag}},
  year      = {2010},
  address   = {Gaithersburg, Maryland, USA},
  month     = mar,
  abstract  = {In this contribution, we propose a new framework to evaluate pedestrian
               simulations by using Extended Range Telepresence. Telepresence is
               used as a virtual reality walking simulator, which provides the user
               with a realistic impression of being present and walking in a virtual
               environment that is much larger than the real physical environment,
               in which the user actually walks. The validation of the simulation
               is performed by comparing motion data of the telepresent user with
               simulated data at some points of the simulation. The use of haptic
               feedback from the simulation makes the framework suitable for training
               in emergency situations.},
  pdf       = {PED10_Perez.pdf}
}

@inproceedings{NIPS09_Deisenroth,
  author    = {Ryan Turner and Marc Peter Deisenroth and Carl Edward Rasmussen},
  title     = {{System Identification in Gaussian Process Dynamical Systems}},
  booktitle = {Nonparametric Bayes Workshop at NIPS 2009},
  year      = {2009},
  address   = {Whistler, Canada},
  month     = dec
}

@inproceedings{ISSPIT09_Baum,
  author    = {Marcus Baum and Uwe D. Hanebeck},
  title     = {{Random Hypersurface Models for Extended Object Tracking}},
  booktitle = {{Proceedings of the 9th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2009)}},
  year      = {2009},
  address   = {Ajman, United Arab Emirates},
  month     = dec,
  abstract  = {Target tracking algorithms usually assume that the received measurements
               stem from a point source. However, in many scenarios  this assumption
               is not feasible so that measurements may stem from different locations,
               named measurement sources, on the target surface. Then, it is necessary
               to incorporate the target extent into the estimation procedure in
               order to obtain robust and precise estimation results. This paper
               introduces the novel concept of Random Hypersurface Models for extended
               targets. A Random Hypersurface Model assumes that each measurement
               source is an element of a randomly generated hypersurface. The applicability
               of this approach is demonstrated by means of an elliptic target shape.
               In this case, a Random Hypersurface Model specifies the random (relative)
               Mahalanobis distance of a measurement source to the center of the
               target object. As a consequence, good estimation results can be obtained
               even if the true target shape significantly differs from the modeled
               shape. Additionally, Random Hypersurface Models are computationally
               tractable with standard nonlinear stochastic state estimators.},
  pdf       = {ISSPIT09_Baum.pdf},
  timestamp = {2009.10.13}
}

@inproceedings{Deisenroth2009,
  author    = {Marc P. Deisenroth and Carl E. Rasmussen},
  title     = {{Efficient Reinforcement Learning for Motor Control}},
  booktitle = {Proceedings of the 10th International PhD Workshop on Systems and Control},
  address   = {Hluboka nad Vltavou, Czech Republic},
  year      = {2009},
  month     = sep
}

@inproceedings{CDC09_HanebeckHuber,
  author    = {Uwe D. Hanebeck and Marco F. Huber and Vesa Klumpp},
  title     = {{Dirac Mixture Approximation of Multivariate Gaussian Densities}},
  booktitle = {Proceedings of the 2009 IEEE Conference on Decision and Control (CDC 2009)},
  year      = {2009},
  address   = {Shanghai, China},
  pdf       = {CDC09_Hanebeck.pdf},
  abstract  = {For the optimal approximation of multivariate
               Gaussian densities by means of Dirac mixtures, i.e., by means of
               a sum of weighted Dirac distributions on a continuous domain,
               a novel systematic method is introduced. The parameters of
               this approximate density are calculated by minimizing a global
               distance measure, a generalization of the well-known Cram\'{e}r-
               von Mises distance to the multivariate case. This generalization
               is obtained by defining an alternative to the classical cumulative
               distribution, the Localized Cumulative Distribution (LCD). In
               contrast to the cumulative distribution, the LCD is unique
               and symmetric even in the multivariate case. The resulting
               deterministic approximation of Gaussian densities by means of
               discrete samples provides the basis for new types of Gaussian
               filters for estimating the state of nonlinear dynamic systems
               from noisy measurements.},
  month     = dec
}

@inproceedings{IROS09_Bogatyrenko,
  author    = {Evgeniya Bogatyrenko and Uwe D. Hanebeck and G{\'{a}}bor Szab{\'{o}}},
  title     = {{Heart Surface Motion Estimation Framework for Robotic Surgery Employing Meshless Methods}},
  booktitle = {{Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009)}},
  month     = oct,
  pdf       = {IROS09_Bogatyrenko.pdf},
  abstract  = {A novel heart surface motion estimation frame-
               work for a robotic surgery on a stabilized beating heart is
               proposed. It includes an approach for the reconstruction and
               prediction of heart surface motion based on a novel physical
               model of the intervention area described by a distributed-
               parameter system. Instead of conventional element methods, a
               meshless method is used for a spatial and temporal decomposi-
               tion of this system. This leads to a finite-dimensional state-space
               form. Furthermore, the state of the resulting lumped-parameter
               system, which provides an approximation of the deflection and
               velocity of the heart surface, is dynamically estimated under
               consideration of uncertainties both occurring in the system
               and arising from noisy camera measurements. By using the
               estimation results, an accurate reconstruction of heart surface
               motion for the synchronisation of the surgical instruments is
               also achieved at occluded or non-measurement points.},
  year      = {2009}
}

@inproceedings{SDF09_Baum,
  author    = {Marcus Baum and Uwe D. Hanebeck},
  title     = {{Tracking an Extended Object Modeled as an Axis-Aligned Rectangle}},
  booktitle = {{4th German Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2009), 39th Annual Conference of the Gesellschaft f{\"u}r Informatik e.V. (GI)}},
  year      = {2009},
  address   = {L{\"u}beck, Germany},
  pdf       = {SDF09_Baum.pdf},
  abstract  = {In many tracking applications, the extent of the target object is neglected
               and it is assumed that the received measurements stem from a point source. However,
               modern sensors are able to supply several measurements from different scattering cen-
               ters on the target object due to their high-resolution capability. As a consequence, it
               becomes necessary to incorporate the target extent into the estimation procedure. This
               paper introduces a new method for tracking the smallest enclosing rectangle of an ex-
               tended object with an unknown shape. At each time step, a finite set of noisy position
               measurements that stem from arbitrary, unknown measurement sources on the target
               surface may be available. In contrast to common approaches, the presented approach
               does not have to make any statistical assumptions on the measurement sources.},
  month     = oct
}

@inproceedings{CCA09_RauhHanebeck,
  author    = {Andreas Rauh and Kai Briechle and Uwe D. Hanebeck},
  title     = {{Nonlinear Measurement Update and Prediction: Prior Density Splitting Mixture Estimator}},
  booktitle = {Proceedings of the 2009 IEEE International Conference on Control Applications (CCA 2009)},
  month     = jul,
  pdf       = {CCA09_RauhHanebeck.pdf},
  year      = {2009},
  abstract  = {In this paper, the Prior Density Splitting Mixture
               Estimator (PDSME), a new Gaussian mixture filtering
               algorithm for nonlinear dynamical systems and nonlinear
               measurement equations, is introduced. This filter reduces the
               linearization error which typically arises if nonlinear state and
               measurement equations are linearized to apply linear filtering
               techniques. For that purpose, the PDSME splits the prior probability
               density into several components of a Gaussian mixture
               with smaller covariances. The PDSME is applicable to both
               prediction and filter steps. A measure for the linearization error
               similar to the Kullback-Leibler distance is introduced allowing
               the user to specify the desired estimation quality. An upper
               bound for the computational effort can be given by limiting
               the maximum number of Gaussian mixture components.}
}

@inproceedings{Fusion09_Baum,
  author    = {Marcus Baum and Uwe D. Hanebeck},
  title     = {{Extended Object Tracking based on Combined Set-Theoretic and Stochastic Fusion}},
  booktitle = {{Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)}},
  address   = {Seattle, Washington, USA},
  month     = jul,
  year      = {2009},
  pdf       = {Fusion09_Baum.pdf},
  abstract  = {In this paper, a novel approach for tracking
               extended objects is presented. The target object is
               modeled as a circular disc such that the center and
               extent of the target object can be estimated. At each
               time step, a finite set of position measurements that
               are corrupted with stochastic noise may be available.
               Each position measurement stems from an unknown measurement
               source on the extended object. In contrast to existing
               approaches, no statistical assumptions about the distribution
               of the measurement sources on the extended object are made.
               As a consequence, it is necessary to deal with stochastic
               and set-valued uncertainties. For this purpose, a novel
               combined stochastic and set-theoretic estimator that employs
               random hyperboloids to express the uncertainties about the
               true circular disc is derived.},
  timestamp = {2009.04.21}
}

@inproceedings{Fusion09_Beutler,
  author    = {Frederik Beutler and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Gaussian Filtering using State Decomposition Methods}},
  booktitle = {{Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)}},
  address   = {Seattle, Washington, USA},
  month     = jul,
  year      = {2009},
  pdf       = {Fusion09_Beutler.pdf},
  abstract  = {State estimation for nonlinear systems generally
               requires approximations of the system or the probability
               densities, as the occurring prediction and filtering equations
               cannot be solved in closed form. For instance, Linear Regression
               Kalman Filters like the Unscented Kalman Filter
               or the considered Gaussian Filter propagate a small set of
               sample points through the system to approximate the posterior
               mean and covariance matrix. To reduce the number of
               sample points, special structures of the system and measurement
               equation can be taken into account. In this paper, two
               principles of system decomposition are considered and applied
               to the Gaussian Filter. One principle exploits that only
               a part of the state vector is directly observed by the measurement.
               The second principle separates the system equations
               into linear and nonlinear parts in order to merely approximate
               the nonlinear part of the state. The benefits of both
               decompositions are demonstrated on a real-world example.},
  timestamp = {2009.04.20}
}

@inproceedings{Fusion09_Hoerst-SGMF,
  author    = {Julian H{\"o}rst and Felix Sawo and Vesa Klumpp and Uwe D. Hanebeck and Dietrich Fr{\"a}nken},
  title     = {{Extension of the Sliced Gaussian Mixture Filter with Application to Cooperative Passive Target Tracking}},
  booktitle = {{Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)}},
  address   = {Seattle, Washington, USA},
  month     = jul,
  year      = {2009},
  pdf       = {Fusion09_Hoerst.pdf},
  abstract  = {This paper copes with the problem of nonlinear Bayesian state estimation.
               A nonlinear filter, the Sliced Gaussian Mixture Filter (SGMF), employs linear
               substructures in the nonlinear measurement and prediction model in order to
               simplify the estimation process.
               Here, a special density representation, the sliced Gaussian mixture
               density, is used to derive an exact solution of the Chapman-Kolmogorov equation.
               The sliced Gaussian mixture density is obtained by a systematic and deterministic
               approximation of a continuous density minimizing a certain distance measure.
               In contrast to previous work, improvements of the SGMF presented here include an
               extended system model and the processing of multi-dimensional nonlinear
               subspaces. As an application for the SGMF, cooperative passive target tracking,
               where sensors take angular measurements from a target, is considered in this paper.
               Finally, the performance of the proposed estimator is compared to the
               marginalized particle filter (MPF) in simulations.},
  timestamp = {2009.04.21}
}

@inproceedings{Fusion09_Huber,
  author    = {Marco F. Huber and Achim Kuwertz and Felix Sawo and Uwe D. Hanebeck},
  title     = {{Distributed Greedy Sensor Scheduling for Model-based Reconstruction of Space-Time Continuous Physical Phenomena}},
  booktitle = {{Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)}},
  address   = {Seattle, Washington, USA},
  month     = jul,
  year      = {2009},
  pdf       = {Fusion09_HuberKuwertz.pdf},
  abstract  = {A novel distributed sensor scheduling method for large-scale sensor
               networks observing space-time continuous physical phenomena is
               introduced. In a first step, the model of the distributed phenomenon
               is spatially and temporally decomposed leading to a linear
               probabilistic finite-dimensional model. Based on this representation,
               the information gain of sensor measurements is evaluated by means of
               the so-called covariance reduction function. For this reward function,
               it is shown that the performance of the greedy sensor scheduling is at
               least half that of the optimal scheduling considering long-term
               effects. This finding is the key for distributed sensor scheduling,
               where a central processing unit or fusion center is unnecessary, and
               thus, scaling as well as reliability is ensured. Hence, greedy
               scheduling in combination with a proposed hierarchical communication
               scheme requires only local sensor information and communication.}
}

@inproceedings{Fusion09_Klumpp-FusionND,
  author    = {Vesa Klumpp and Uwe D. Hanebeck},
  title     = {{Nonlinear Fusion of Multi-Dimensional Densities in Joint State Space}},
  booktitle = {{Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)}},
  address   = {Seattle, Washington, USA},
  month     = jul,
  year      = {2009},
  timestamp = {2009.04.21},
  pdf       = {Fusion09_Klumpp-FusionND.pdf},
  abstract  = {Nonlinear fusion of multi-dimensional densities
               is an important application in Bayesian state estimation.
               In the approach proposed here, a joint density over all
               considered densities is build, which is then approximated
               by means of a Dirac mixture density by partitioning the
               joint state space into regions that are represented by single
               Dirac components. This approximation procedure depends
               on the nonlinear fusion model and only areas relevant to this
               model are considered. The processing in joint state space
               has advantages, especially when fusing Dirac mixture densities.
               Within this approach, degeneration can be avoided
               and even densities without mutual support can be combined.
               Thus, this approach gives an alternative to multiplication of
               Dirac mixtures with a likelihood, as used in the particle filter.
               Furthermore, a nonlinear Bayesian estimator with filter
               and prediction step can be formulated, which is able to cope
               with both discrete and continuous densities.}
}

@inproceedings{Fusion09_Klumpp-Type2Dens,
  author    = {Vesa Klumpp and Uwe D. Hanebeck},
  title     = {{Bayesian Estimation with Uncertain Parameters of Probability Density Functions}},
  booktitle = {{Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)}},
  address   = {Seattle, Washington, USA},
  month     = jul,
  year      = {2009},
  pdf       = {Fusion09_Klumpp-Type2Dens.pdf},
  abstract  = {In this paper, we address the problem of
               processing imprecisely known probability density func-
               tions by means of Bayesian estimation. The imprecise
               knowledge about probability density functions is given
               as stochastic uncertainty about their parameters. The
               proposed processing of this special density in a Bayesian
               estimator is accomplished by reinterpretation of the Fil-
               ter and prediction equations. Here, the parameters are
               treated as a higher order state, which can be processed
               by Bayesian estimation techniques. For state estima-
               tion, this avoids the need to select specific values for
               unknown parameters and, thus, allows the processing of
               all potential parameters at once. The proposed approach
               further allows the use of imprecisely known model equa-
               tions for measurement and state prediction by the same
               principle.}
}

@inproceedings{Fusion09_Krauthausen,
  author    = {Peter Krauthausen and Uwe D. Hanebeck},
  title     = {{Intention Recognition for Partial-Order Plans Using Dynamic Bayesian Networks}},
  booktitle = {{Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)}},
  address   = {Seattle, Washington, USA},
  month     = jul,
  year      = {2009},
  pdf       = {Fusion09_Krauthausen.pdf},
  abstract  = {In this paper, a novel probabilistic ap-
               proach to intention recognition for partial-order plans
               is proposed. The key idea is to exploit independences
               between subplans to substantially reduce the state space
               sizes in the compiled Dynamic Bayesian Networks.
               This makes inference more efficient. The main con-
               tributions are the computationally exploitable definition
               of subplan structures, the introduction of a novel Lay-
               ered Intention Model and a Dynamic Bayesian Net-
               work representation with an inference mechanism that
               exploits consecutive and concurrent subplans' indepen-
               dences. The presented approach reduces the state space
               to the order of the most complex subplan and requires
               only minor changes in the standard inference mecha-
               nism. The practicability of this approach is demon-
               strated by recognizing the process of shelf-assembly.}
}

@inproceedings{Fusion09_Noack,
  author    = {Benjamin Noack and Vesa Klumpp and Uwe D. Hanebeck},
  title     = {{State Estimation with Sets of Densities considering Stochastic and Systematic Errors}},
  booktitle = {Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)},
  address   = {Seattle, Washington, USA},
  year      = {2009},
  month     = jul,
  pdf       = {Fusion09_Noack.pdf},
  abstract  = {In practical applications, state estimation requires the consideration of
               stochastic and systematic errors. If both error types are present, an exact
               probabilistic description of the state estimate is not possible, so that
               common Bayesian estimators have to be questioned. This paper introduces a
               theoretical concept, which allows for incorporating unknown but bounded errors
               into a Bayesian inference scheme by utilizing sets of densities. In order to
               derive a tractable estimator, the Kalman filter is applied to ellipsoidal sets
               of means, which are used to bound additive systematic errors. Also, an
               extension to nonlinear system and observation models with ellipsoidal error
               bounds is presented. The derived estimator is motivated by means of two
               example applications.}
}

@inproceedings{Fusion09_Schieferdecker,
  author    = {Dennis Schieferdecker and Marco F. Huber},
  title     = {{Gaussian Mixture Reduction via Clustering}},
  booktitle = {{Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)}},
  address   = {Seattle, Washington, USA},
  month     = jul,
  year      = {2009},
  pdf       = {Fusion09_SchieferdeckerHuber.pdf},
  abstract  = {Recursive processing of Gaussian mixture functions inevitably leads to a
               large number of mixture components. In order to keep the computational
               complexity at a feasible level, the number of their components has to be
               reduced periodically. There already exists a variety of algorithms for
               this purpose, bottom-up and top-down approaches, methods that take the
               global structure of the mixture into account or that work locally and
               consider few mixture components at the same time. The mixture reduction
               algorithm presented in this paper can be categorized as global top-down
               approach. It takes a clustering algorithm originating from the field of
               theoretical computer science and adapts it for the problem of Gaussian
               mixture reduction. The achieved results are on the same scale as the
               results of the current "state-of-the-art" algorithm PGMR, but, depending
               on the input size, the whole procedure performs significantly faster.},
  timestamp = {2009.04.20}
}

@inproceedings{MSRL09_Deisenroth,
  author    = {Marc Peter Deisenroth and Carl Edward Rasmussen},
  title     = {{Bayesian Inference for Efficient Learning in Control}},
  booktitle = {{Multidisciplinary Symposium on Reinforcement Learning (MSRL)}},
  year      = {2009},
  address   = {Montreal, Canada},
  month     = jun,
  pdf       = {PHDWS09_Deisenroth.pdf}
}

@inproceedings{ICINCO09_Perez,
  author    = {Antonia P\'{e}rez Arias and Uwe D. Hanebeck},
  title     = {{A Novel Haptic Interface for Extended Range Telepresence:
               Control and Evaluation}},
  booktitle = {Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2009)},
  year      = {2009},
  pages     = {222--227},
  address   = {Milan, Italy},
  month     = jul,
  pdf       = {ICINCO09_Perez.pdf},
  abstract  = {A novel haptic interface for extended range telepresence
               is presented that allows the user simultaneous wide area motion and
               haptic interaction in remote environments. To achieve an extended
               workspace, the haptic interface consists of a haptic manipulator for
               precise haptic rendering and a large portal carrier system that
               enlarges the workspace by prepositioning the end-effector. As the
               prepositioning unit is grounded and driven by three linear drives, our
               approach has the advantages of high force capability, and an accurate
               positioning of the haptic interface. The use of this haptic interface
               with Motion Compression permits to explore large remote environments
               even from small user environments. As a result, not only has the user
               visual, acoustic, and haptic feedback but can also control the
               teleoperator or avatar by natural walking, which considerably increases
               the sense of immersion. A prototype system for haptic extended range
               telepresence was designed, implemented, and tested.},
  keywords  = {Extended range telepresence, motion compression, haptic interface, force control}
}

@inproceedings{ICML09_Deisenroth,
  author    = {Marc P. Deisenroth and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Analytic Moment-based Gaussian Process Filtering}},
  booktitle = {{26th International Conference on Machine Learning (ICML 2009) in Montreal, Canada}},
  month     = jun,
  year      = {2009},
  abstract  = {We propose an analytic moment-based filter for nonlinear stochastic
               dynamic systems modeled by Gaussian processes. Exact expressions for the
               expected value and the covariance matrix are provided for both the
               prediction step and the filter step, where an additional Gaussian
               assumption is exploited in the latter case. Our filter does not require
               further approximations. In particular, it avoids finite-sample
               approximations. We compare the filter to a variety of Gaussian filters,
               that is, the EKF, the UKF, and the recent GP-UKF proposed by Ko et al.
               (2007).},
  pdf       = {ICML09_DeisenrothHuber.pdf}
}

@inproceedings{CASA09_Perez,
  author    = {Antonia P\'{e}rez Arias and Tobias Kretz and Peter Ehrhardt and Stefan Hengst and Peter Vortisch and Uwe D. Hanebeck},
  title     = {{A Framework for Evaluating the VISSIM Traffic Simulation with Extended Range Telepresence}},
  booktitle = {Proceedings of the 22nd Annual Conference on Computer Animation and Social Agents (CASA 2009)},
  year      = {2009},
  address   = {Amsterdam, The Netherlands},
  month     = jun,
  pages     = {13--16},
  pdf       = {CASA09_Perez.pdf},
  abstract  = {This paper presents a novel framework
               for combining traffic simulations and extended range
               telepresence. The real user's position data can thus
               be used for validation and calibration of models of
               pedestrian dynamics, while the user experiences a
               high degree of immersion by interacting with agents
               in realistic simulations.}
}

@inproceedings{ICASSP09_Beutler,
  author    = {Frederik Beutler and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Instantaneous Pose Estimation using Rotation Vectors}},
  booktitle = {{IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009) in Taipei, Taiwan}},
  month     = apr,
  year      = {2009},
  pdf       = {ICASSP09_Beutler.pdf},
  abstract  = {An algorithm for estimating the pose, i.e., translation and rotation, of
               an extended target object is introduced. Compared to conventional
               methods, where pose estimation is performed on the basis of timeof-
               flight (TOF) measurements between external sources and sensors
               attached to the object, the proposed approach directly uses the amplitude
               values measured at the sensors for estimation purposes without
               an intermediate TOF estimation step. This is achieved by modeling
               the wave propagation by a nonlinear dynamic system comprising a
               system and a measurement equation. The nonlinear system equation
               includes a model of the time-variant structure of the object rotation
               based on rotation vectors. As a result, the measured amplitude values
               at the sensors can be processed instantaneously in a recursive
               fashion. Uncertainties in the measurement process are systematically
               considered by employing a stochastic filter for estimating the
               pose, i.e., the state of the nonlinear dynamic system.},
  pages     = {3413--3416}
}

@article{MFI09_Beutler,
  author   = {Frederik Beutler and Marco F. Huber and Uwe D. Hanebeck},
  title    = {Probabilistic Instantaneous Model-Based Signal Processing applied to Localization and Tracking},
  journal  = {{Journal of Robotics and Autonomous Systems, Selected papers from 2006 IEEE International Conference on Multisensor Fusion and Integration (MFI 2006)}},
  month    = mar,
  year     = {2009},
  volume   = {57},
  number   = {3},
  pages    = {249--258},
  abstract = {In this paper, a probabilistic approach for estimating time
              and space-variant parameters of a system, based on sequentially received
              discrete-time signal values, is presented. The system description is the
              solution of a linear partial differential equation (PDE). The PDE describes
              for example the wave propagation of an acoustic wave in a localization
              system. The solution of the PDE is given by a time-variant and space-variant
              impulse response. This impulse response is characterized by the time and
              space-variant parameters in order to track an object, which emits for example
              an acoustic signal. For estimating the position of the object in an
              instantaneous way a Bayesian approach has to be used, which considers the
              dynamic behavior of the parameters in a system model and uncertainties in a
              stochastic manner by means of probability density functions. Hence, the new
              approach provides a probabilistic instantaneous model-based signal processing,
              where the sequentially measured signal values are processed directly and known
              reference signal sequences are interpreted as part of a time-variant nonlinear
              measurement equation.},
  url      = {https://dx.doi.org/10.1016/j.robot.2008.10.013},
  note     = {doi:10.1016/j.robot.2008.10.013}
}

@book{MFI09_Hanebeck,
  title     = {Robotics and Autonomous Systems},
  publisher = {ScienceDirect},
  volume    = {57},
  number    = {3},
  pages     = {237--344},
  year      = {2009},
  editor    = {Uwe D. Hanebeck and Thomas C. Henderson},
  series    = {Selected papers from 2006 IEEE International Conference on Multisensor Fusion and Integration (MFI 2006), 2006 IEEE International Conference on Multisensor Fusion and Integration},
  month     = sep,
  pdf       = {MFI09_Hanebeck.pdf}
}

@article{NC09_Deisenroth,
  author    = {Marc P. Deisenroth and Carl E. Rasmussen and Jan Peters},
  title     = {Gaussian {Process Dynamic Programming}},
  journal   = {Neurocomputing},
  year      = {2009},
  volume    = {72},
  pages     = {1508--1524},
  number    = {7--9},
  month     = mar,
  abstract  = {Reinforcement learning (RL) and optimal control of systems with continuous
               states and actions require approximation techniques in most interesting	cases.
               In this article, we introduce Gaussian process dynamic programming '(GPDP), an
               approximate value-function based RL algorithm. We consider both a classic optimal
               control problem, where problem-specific prior knowledge is available,
               and a classic RL problem, where only very general priors can be used.
               For the classic optimal control problem, GPDP models the unknown value
               functions with Gaussian processes and generalizes dynamic programming to continuous-valued
               states and actions. For the RL problem, GPDP starts from a given initial state
               and explores the state space using Bayesian active learning. To
               design a fast learner, available data has to be used efficiently.
               Hence, we propose to learn probabilistic models of the a priori unknown
               transition dynamics and the value functions on the fly. In both
               cases, we successfully apply the resulting continuous-valued controllers
               to the under-actuated pendulum swing up and analyze the performances of the
               suggested algorithms. It turns out that GPDP uses data very efficiently and
               can be applied to problems, where classic dynamic programming would be cumbersome.},
  doi       = {10.1016/j.neucom.2008.12.019},
  timestamp = {2008.06.16},
  pdf       = {Neurocomputing09_DeisenrothRasmussenPeters_preprint.pdf}
}

@incollection{LNEE_ICINCO08_Schrempf,
  author    = {Oliver C. Schrempf and Uwe D. Hanebeck},
  title     = {{Dirac Mixture Approximation for Nonlinear Stochastic Filtering}},
  booktitle = {Informatics in Control, Automation and Robotics --- Selected Papers from the International Conference on Informatics in Control, Automation and Robotics 2007},
  publisher = {Springer},
  year      = {2008},
  series    = {Lecture Notes in Electrical Engineering},
  volume    = {24},
  month     = sep,
  pages     = {287--300},
  timestamp = {2007.12.19},
  abstract  = {This work presents a filter for estimating the state of
               nonlinear dynamic systems. It is based on optimal recursive approximation
               the state densities by means of Dirac mixture functions in order to allow
               for a closed form solution of the prediction and filter step. The
               approximation approach is based on a systematic minimization of a distance
               measure and is hence optimal and deterministic. In contrast to non-deterministic
               methods we are able to determine the optimal number of components in the Dirac
               mixture. A further benefit of the proposed approach is the consideration of
               measurements during the approximation process in order to avoid parameter degradation.},
  url       = {https://link.springer.com/chapter/10.1007/978-3-540-85640-5_22}
}

@incollection{LNEE_ICINCO08_WeisselHuber,
  author    = {Florian Weissel and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Stochastic Nonlinear Model Predictive Control based on Gaussian Mixture Approximations}},
  booktitle = {Informatics in Control, Automation and Robotics --- Selected Papers from the International Conference on Informatics in Control, Automation and Robotics 2007},
  publisher = {Springer},
  year      = {2008},
  series    = {Lecture Notes in Electrical Engineering},
  volume    = {24},
  month     = sep,
  pages     = {239--252},
  timestamp = {2007.12.19},
  abstract  = {In this paper, a framework for stochastic Nonlinear
               Model Predictive Control (NMPC) that explicitly incorporates the
               noise influence on systems with continuous state spaces is introduced.
               By the incorporation of noise, which results from uncertainties during
               model identification and measurement, the quality of control can be
               significantly increased. Since stochastic NMPC requires the prediction
               of system states over a certain horizon, an efficient state prediction
               technique for nonlinear noise-affected systems is required. This is
               achieved by using transition densities approximated by axis-aligned
               Gaussian mixtures together with methods to reduce the computational burden.
               A versatile cost function representation also employing Gaussianmixtures
               provides an increased freedom of modeling. Combining the rediction technique
               with this value function representation allows closed-form calculation of
               the necessary optimization problems arising from stochastic NMPC. The
               capabilities of the framework and especially the benefits that can be
               gained by considering the noise in the controller are illustrated by the
               example of a mobile robot following a given path.},
  url       = {https://link.springer.com/chapter/10.1007/978-3-540-85640-5_18}
}

@inproceedings{MFI08_Hanebeck-LCD,
  author    = {Uwe D. Hanebeck and Vesa Klumpp},
  title     = {{Localized Cumulative Distributions and a Multivariate Generalization of the Cram\'{e}r-von Mises Distance}},
  booktitle = {Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = aug,
  pdf       = {MFI08_Hanebeck-LCD.pdf},
  pages     = {33--39},
  abstract  = {This paper is concerned with distances for comparing
               multivariate random vectors with a special focus on the case
               that at least one of the random vectors is of discrete type, i.e.,
               assumes values from a discrete set only. The first contribution
               is a new type of characterization of multivariate random
               quantities, the so called Localized Cumulative Distribution
               (LCD) that, in contrast to the conventional definition of a
               cumulative distribution, is unique and symmetric. Based on the
               LCDs of the random vectors under consideration, the second
               contribution is the definition of generalized distance measures
               that are suitable for the multivariate case. These distances
               are used for both analysis and synthesis purposes. Analysis
               is concerned with assessing whether a given sample stems from
               a given continuous distribution. Synthesis is concerned with
               both density estimation, i.e., calculating a suitable continuous
               approximation of a given sample, and density discretization,
               i.e., approximation of a given continuous random vector by a
               discrete one.},
  timestamp = {2008.06.27}
}

@inproceedings{MFI08_HuberBailey,
  author    = {Marco F. Huber and Tim Bailey and Hugh Durrant-Whyte and Uwe D. Hanebeck},
  title     = {{On Entropy Approximation for Gaussian Mixture Random Vectors}},
  booktitle = {Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = aug,
  pdf       = {MFI08_HuberBailey.pdf},
  pages     = {181--188},
  abstract  = {For many practical probability density representations
               such as for the widely used Gaussian mixture densities, an
               analytic evaluation of the differential entropy is not possible and
               thus, approximate calculations are inevitable. For this purpose,
               the first contribution of this paper deals with a novel entropy
               approximation method for Gaussian mixture random vectors,
               which is based on a component-wise Taylor-series expansion of
               the logarithm of a Gaussian mixture and on a splitting method
               of Gaussian mixture components. The employed order of the
               Taylor-series expansion and the number of components used for
               splitting allows balancing between accuracy and computational
               demand. The second contribution is the determination of meaningful
               and efficiently to calculate lower and upper bounds of the
               entropy, which can be also used for approximation purposes.
               In addition, a refinement method for the more important upper
               bound is proposed in order to approach the true entropy value.},
  timestamp = {2008.06.27}
}

@inproceedings{MFI08_Klumpp-DiracFusion,
  author    = {Vesa Klumpp and Uwe D. Hanebeck},
  title     = {{Direct Fusion of Dirac Mixture Densities using an Efficient Approximation in Joint State Space}},
  booktitle = {Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = aug,
  pdf       = {MFI08_Klumpp-DiracFusion.pdf},
  pages     = {168--174},
  abstract  = {In this paper, we present a direct fusion algorithm
               for processing the combination of two Dirac mixture densities.
               The proposed approach allows the multiplication of two Dirac
               mixture densities without requiring identical support and thus
               enables the fusion of two independently generated sample sets.
               The resulting posterior Dirac mixture density is an approximation
               of the true continuous density that would result from the
               processing of the underlying true continuous density functions.
               This procedure is based on a suboptimal greedy approximation
               of the joint state space by means of a Dirac mixture that
               iteratively increases the resolution of the fusion result while
               considering only the relevant regions in the joint state space,
               where the fusion constraint holds.},
  timestamp = {2008.06.27}
}

@inproceedings{MFI08_Klumpp-DiracTrees,
  author    = {Vesa Klumpp and Uwe D. Hanebeck},
  title     = {{Dirac Mixture Trees for Fast Suboptimal Multi-Dimensional Density Approximation}},
  booktitle = {Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = aug,
  pdf       = {MFI08_Klumpp-DiracTrees.pdf},
  pages     = {593--600},
  abstract  = {We consider the problem of approximating an
               arbitrary multi-dimensional probability density function by
               means of a Dirac mixture density. Instead of an optimal
               solution based on minimizing a global distance measure between
               the true density and its approximation, a fast suboptimal
               anytime procedure is proposed, which is based on sequentially
               partitioning the state space and component placement by local
               optimization. The proposed procedure adaptively covers the
               entire state space with a gradually increasing resolution. It
               can be efficiently implemented by means of a pre-allocated
               tree structure in a straightforward manner. The resulting computational
               complexity is linear in the number of components
               and linear in the number of dimensions. This allows a large
               number of components to be handled, which is especially useful
               in high-dimensional state spaces.},
  timestamp = {2008.06.27}
}

@inproceedings{MFI08_NaWang,
  author    = {Chongning Na and Hui Wang and Dragan Obradovic and Uwe D. Hanebeck},
  title     = {{Fourier Density Approximation for Belief Propagation in Wireless Sensor Networks}},
  booktitle = {Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  pages     = {290--295},
  abstract  = {Many distributed inference problems in wireless
               sensor networks can be represented by probabilistic graphical
               models, where belief propagation, an iterative message passing
               algorithm provides a promising solution. In order to make the
               algorithm efficient and accurate, messages which carry the
               belief information from one node to the others should be
               formulated in an appropriate format. This paper presents two
               belief propagation algorithms where non-linear and
               non-Gaussian beliefs are approximated by Fourier density
               approximations, which significantly reduces power
               consumptions in the belief computation and transmission. We
               use self-localization in wireless sensor networks as an example to
               illustrate the performance of this method.},
  url       = {https://dx.doi.org/10.1109/MFI.2008.4648080},
  pdf       = {MFI08_NaWang.pdf},
  month     = aug
}

@inproceedings{MFI08_Sawo-Localization,
  author    = {Felix Sawo and Thomas C. Henderson and Christopher Sikorski and Uwe D. Hanebeck},
  title     = {{Sensor Node Localization Methods based on Local Observations of Distributed Natural Phenomena}},
  booktitle = {Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = aug,
  timestamp = {2008.06.27},
  pdf       = {MFI08_Sawo-Localization.pdf},
  pages     = {301--308},
  abstract  = {This paper addresses the model-based localization
               of sensor networks based on local observations of a distributed
               phenomenon. For the localization process, we propose the
               rigorous exploitation of strong mathematical models of distributed
               phenomena. By unobtrusively exploiting background
               phenomena, the individual sensor nodes can be localized by
               only observing its local surrounding without the necessity of
               heavy infrastructure. In this paper, we introduce two novel
               approaches: (a) the polynomial system localization method
               (PSL-method) and (b) the simultaneous reconstruction and
               localization method (SRL-method). The first approach (PSLmethod)
               is based on restating the mathematical model of the
               distributed phenomenon in terms of a polynomial system.
               These equations depend on both the state of the phenomenon
               and the node locations. Solving the system of polynomials
               for each individual sensor node directly leads to the desired
               locations. The second approach (SRL-method) basically regards
               the localization problem as a simultaneous state and parameter
               estimation problem with the node locations as parameters. By
               this means, the distributed phenomenon is reconstructed and
               the individual nodes are localized in a simultaneous fashion.
               In addition, within this framework the uncertainties in the
               mathematical model and the measurements are considered.
               The performance of the two different localization approaches
               is demonstrated by means of simulation results.},
  annote    = {<b>Winner Best Paper Award</b> <a href="https://isas.iar.kit.edu/pdf/award/MFI08_BestPaperAward_Cerificate.pdf" target="_blank">Certificate (PDF)</a>}
}

@inproceedings{MFI08_Schwarzenberg,
  author    = {Gregor F. Schwarzenberg and Uwe Mayer and Nicole V. Ruiter and Uwe D. Hanebeck},
  title     = {{3D Reflectivity Reconstruction by Means of Spatially Distributed Kalman Filters}},
  booktitle = {Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = aug,
  pages     = {384--391},
  abstract  = {In seismic, radar, and sonar imaging the exact determination
               of the reflectivity distribution is usually intractable
               so that approximations have to be applied. A method called
               synthetic aperture focusing technique (SAFT) is typically used
               for such applications as it provides a fast and simple method to
               reconstruct (3D) images. Nevertheless, this approach has several
               drawbacks such as causing image artifacts as well as offering
               no possibility to model system-specific uncertainties.
               In this paper, a statistical approach is derived, which models
               the region of interest as a probability density function (PDF)
               representing spatial reflectivity occurrences. To process the
               nonlinear measurements, the exact PDF is approximated by
               well-placed Extended Kalman Filters allowing for efficient and
               robust data processing.
               The performance of the proposed method is demonstrated
               for a 3D ultrasound computer tomograph and comparisons are
               carried out with the SAFT image reconstruction.},
  pdf       = {MFI08_Schwarzenberg.pdf}
}

@inproceedings{MFI08_WangSzabo,
  author    = {Hui Wang and Andrei Szabo and Joachim Bamberger and Uwe D. Hanebeck},
  title     = {{Simultaneous Multi-Information Fusion and Parameter Estimation for Robust 3-D Indoor Positioning Systems}},
  booktitle = {Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  pages     = {445--450},
  abstract  = {Typical WLAN based indoor positioning systems
               take the received signal strength (RSS) as the major information
               source. Due to the complicated indoor environment, the RSS
               measurements are hard to model and too noisy to achieve a
               satisfactory 3-D accuracy in multi-floor scenarios. To enhance
               the performance of WLAN positioning systems, extra information
               sources could be integrated. In this paper, a Bayesian
               framework is applied to fuse multi-information sources and
               estimate the spatial and time varying parameters simultaneously
               and adaptively. An application of this framework, which
               fuses pressure measurements, a topological building map with
               RSS measurements, and simultaneously estimates the pressure
               sensor bias, is investigated. Our experiments indicate that the
               localization performance is more accurate and robust by using
               our approach.},
  url       = {https://dx.doi.org/10.1109/MFI.2008.4648035},
  pdf       = {MFI08_WangSzabo.pdf},
  month     = aug
}

@inproceedings{MFI08_WeisselSchreiter,
  author    = {Florian Weissel and Thomas Schreiter and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Stochastic Model Predictive Control of Time-Variant Nonlinear Systems with Imperfect State Information}},
  booktitle = {Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = aug,
  pdf       = {MFI08_WeisselSchreiter.pdf},
  pages     = {40--46},
  abstract  = {In many technical systems, the system state, which
               is to be controlled, is not directly accessible, but has to be
               estimated from observations. Furthermore, the uncertainties
               arising from this procedure are typically neglected in the
               controller. To remedy this deficiency, in this paper, we present a
               novel approach to stochastic nonlinear model predictive control
               (NMPC) for heavily noise-affected systems with not directly
               accessible, i.e., hidden states, extending the stochastic NMPCframework
               presented in [1]. An important property of our novel
               method is that, in contrast to classical approaches, time-variant
               system and measurement equations as well as time-variant step
               rewards can be considered. Extending the techniques from
               [1] by introducing virtual future observations and combining
               this with a novel tree search algorithm, called probabilistic
               branch-and-bound search (PBAB), a solution with a feasible
               computational demand of the challenging problem is possible.}
}

@inproceedings{IFAC08_Huber,
  author    = {Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Gaussian Filter based on Deterministic Sampling for High Quality Nonlinear Estimation}},
  booktitle = {Proceedings of the 17th IFAC World Congress (IFAC 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = jul,
  pdf       = {IFAC08_Huber.pdf},
  volume    = {17},
  number    = {2},
  abstract  = {In this paper, a Gaussian filter for nonlinear Bayesian estimation is introduced that is
               based on a deterministic sample selection scheme. For an effective sample selection, a parametric
               density function representation of the sample points is employed, which allows approximating the
               cumulative distribution function of the prior Gaussian density. The computationally demanding
               parts of the optimization problem formulated for approximation are carried out off-line for
               obtaining an efficient filter, whose estimation quality can be altered by adjusting the number
               of used sample points. The improved performance of the proposed Gaussian filter compared to
               the well-known unscented Kalman fiter is demonstrated by means of two examples.}
}

@inproceedings{IFAC08_SawoBeutler,
  author    = {Felix Sawo and Frederik Beutler and Uwe D. Hanebeck},
  title     = {{Decentralized State Estimation of Distributed Phenomena based on Covariance Bounds}},
  booktitle = {Proceedings of the 17th IFAC World Congress (IFAC 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = jul,
  pdf       = {IFAC08_SawoBeutler.pdf},
  volume    = {17},
  number    = {2},
  abstract  = {This paper addresses the problem of decentralized state estimation of distributed physical
               phenomena observed by a sensor network. The centralized approaches are not scalable for large
               sensor networks, because all information has to be transmitted to a powerful central processing node
               requiring an extensive amount of communication bandwidth and a lot of processing power. Thus, for a
               decentralized reconstruction of distributed phenomena, we propose a novel methodology consisting of
               three steps: (a) conversion of the distributed phenomenon into a lumped-parameter system description,
               (b) decomposition of the resulting system in order to map the description to the actual sensor network,
               and (c) decomposition of the density representation leading to a decentralized estimation approach. The
               main problem of a decentralized approach is that due to the propagation of local information through the
               network, unknown correlations are caused. This fact needs to be considered during the reconstruction
               process in order to get correct and consistent estimation results. For that reason, we employ a robust
               estimator (based on Covariance Bounds) for the local reconstruction update on each sensor node. By this
               means, the individual sensor nodes are able to estimate the local state of the distributed phenomenon
               using local estimates obtained and communicated by adjacent nodes only. The information about their
               correlations is not stored in the sensor network.}
}

@inproceedings{IFAC08_WeisselHuber,
  author    = {Florian Weissel and Marco F. Huber and Dietrich Brunn and Uwe D. Hanebeck},
  title     = {{Stochastic Optimal Control based on Value-Function Approximation using Sinc Interpolation}},
  booktitle = {Proceedings of the 17th IFAC World Congress (IFAC 2008)},
  year      = {2008},
  address   = {Seoul, Republic of Korea},
  month     = jul,
  pdf       = {IFAC08_WeisselHuber.pdf},
  volume    = {17},
  number    = {2},
  abstract  = {An effcient approach for solving stochastic optimal control problems is to employ
               dynamic programming (DP). For continuous-valued nonlinear systems, the corresponding
               DP recursion generally cannot be solved in closed form. Thus, a typical approach is to
               discretize the DP value functions in order to be able to carry out the calculation. Especially
               for multidimensional systems, either a large number of discretization points is necessary or
               the quality of approximation degrades. This problem can be alleviated by interpolating the
               discretized value function. In this paper, we present an approach based on optimal low-pass
               interpolation employing sinc functions (sine cardinal). For the important case of systems with
               Gaussian mixture noise (including the special case of Gaussian noise), we show how the
               calculations required for this approach, especially the nontrivial calculation of an expected
               value of a Gaussian mixture random variable transformed by a sinc function, can be carried out
               analytically. We illustrate the effectiveness of the proposed interpolation scheme by an example
               from the field of Stochastic Nonlinear Model Predictive Control (SNMPC).}
}

@inproceedings{Fusion08_Huber-PGMR,
  author    = {Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Progressive Gaussian Mixture Reduction}},
  booktitle = {Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)},
  year      = {2008},
  address   = {Cologne, Germany},
  month     = jul,
  pdf       = {Fusion08_Huber-PGMR.pdf},
  pages     = {1--8},
  abstract  = {For estimation and fusion tasks it is inevitable to
               approximate a Gaussian mixture by one with fewer components
               to keep the complexity bounded. Appropriate approximations
               can be typically generated by exploiting the redundancy in
               the shape description of the original mixture. In contrast to
               the common approach of successively merging pairs of components
               to maintain a desired complexity, the novel Gaussian
               mixture reduction algorithm introduced in this paper avoids
               to directly reduce the original Gaussian mixture. Instead, an
               approximate mixture is generated from scratch by employing
               homotopy continuation. This allows starting the approximation
               with a single Gaussian, which is constantly adapted to the
               progressively incorporated true Gaussian mixture. Whenever a
               user-defined bound on the deviation of the approximation cannot
               be maintained during the continuation, further components are
               added to the approximation. This facilitates significantly reducing
               the number of components even for complex Gaussian mixtures.}
}

@inproceedings{Fusion08_Huber_PLSS,
  author    = {Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Priority List Sensor Scheduling using Optimal Pruning}},
  booktitle = {Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)},
  year      = {2008},
  address   = {Cologne, Germany},
  month     = jul,
  pdf       = {Fusion08_Huber_PLSS.pdf},
  pages     = {1--8},
  abstract  = {State estimation and reconstruction quality of distributed
               phenomena that are monitored by a network of distributed
               sensors is strongly affected by communication failures,
               which is a problem in real-world sensor networks. In this paper,
               we propose a novel sensor scheduling approach named priority
               list sensor scheduling (PLSS). This approach facilitates efficient
               distributed estimation in sensor networks, even in case of unreliable
               communication, by prioritizing the sensor nodes according
               to local sensor schedules based on the predicted estimation
               error. It is shown that PLSS minimizes the expected estimation
               error for arbitrary packet-loss or transmission probabilities.
               As prioritizing sensor nodes requires the calculation of several
               sensor schedules, a novel pruning algorithm that preserves
               optimal schedules is also derived in order to significantly reduce
               the computational demand. This is accomplished by exploiting
               the monotonicity of the Riccati equation and the information
               contribution of individual sensor nodes in combination with a
               branch-and-bound technique.}
}

@inproceedings{Fusion08_KlumppSawo-SGMF,
  author    = {Vesa Klumpp and Felix Sawo and Uwe D. Hanebeck and Dietrich Fr{\"a}nken},
  title     = {{The Sliced Gaussian Mixture Filter for Efficient Nonlinear Estimation}},
  booktitle = {Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)},
  year      = {2008},
  address   = {Cologne, Germany},
  month     = jul,
  pdf       = {Fusion08_KlumppSawo-SGMF.pdf},
  pages     = {1--8},
  abstract  = {This paper addresses the efficient state estimation
               for mixed linear/nonlinear dynamic systems with noisy measurements.
               Based on a novel density representation - sliced Gaussian
               mixture density - the decomposition into a (conditionally) linear
               and nonlinear estimation problem is derived. The systematic
               approximation procedure minimizing a certain distance measure
               allows the derivation of (close to) optimal and deterministic
               estimation results. This leads to high-quality representations of
               the measurement-conditioned density of the states and, hence, to
               an overall more efficient estimation process. The performance of
               the proposed estimator is compared to state-of-the-art estimators,
               like the well-known marginalized particle filter.}
}

@inproceedings{Fusion08_Noack-ConvexSets,
  author    = {Benjamin Noack and Vesa Klumpp and Dietrich Brunn and Uwe D. Hanebeck},
  title     = {{Nonlinear Bayesian Estimation with Convex Sets of Probability Densities}},
  booktitle = {Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)},
  year      = {2008},
  address   = {Cologne, Germany},
  month     = jul,
  pdf       = {Fusion08_Noack.pdf},
  pages     = {1--8},
  abstract  = {This paper presents a theoretical framework for
               Bayesian estimation in the case of imprecisely known probability
               density functions. The lack of knowledge about the true density
               functions is represented by sets of densities. A formal Bayesian
               estimator for these sets is introduced, which is intractable for
               infinite sets. To obtain a tractable filter, properties of convex
               sets in form of convex polytopes of densities are investigated.
               It is shown that pathwise connected sets and their convex hulls
               describe the same ignorance. Thus, an exact algorithm is derived,
               which only needs to process the hull, delivering tractable results
               in the case of a proper parametrization. Since the estimator
               delivers a convex hull of densities as output, the theoretical
               grounds are laid for deriving efficient Bayesian estimators for
               sets of densities. The derived filter is illustrated by means of an
               example.}
}

@inproceedings{Fusion08_SawoKlumpp-SimParam,
  author    = {Felix Sawo and Vesa Klumpp and Uwe D. Hanebeck},
  title     = {{Simultaneous State and Parameter Estimation of Distributed-Parameter Physical Systems based on Sliced Gaussian Mixture Filter}},
  booktitle = {Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)},
  year      = {2008},
  address   = {Cologne, Germany},
  month     = jul,
  pdf       = {Fusion08_SawoKlumpp-SimParam.pdf},
  pages     = {1--8},
  abstract  = {This paper presents a method for the simultaneous
               state and parameter estimation of finite-dimensional models of
               distributed systems monitored by a sensor network. In the
               first step, the distributed system is spatially and temporally
               decomposed leading to a linear finite-dimensional model in state
               space form. The main challenge is that the simultaneous state and
               parameter estimation of such systems leads to a high-dimensional
               nonlinear problem. Thanks to the linear substructure contained
               in the resulting finite-dimensional model, the development of an
               overall more efficient estimation process is possible. Therefore,
               in the second step, we propose the application of a novel density
               representation - sliced Gaussian mixture density - in order to
               decompose the estimation problem into a (conditionally) linear
               and a nonlinear problem. The systematic approximation procedure
               minimizing a certain distance measure allows the derivation
               of (close to) optimal and deterministic results. The proposed
               estimation process provides novel prospects in sensor network
               applications. The performance is demonstrated by means of
               simulation results.},
  timestamp = {2008.04.29}
}

@inproceedings{Fusion08_Wang,
  author    = {Hui Wang and Andrei Szabo and Joachim Bamberger and Dietrich Brunn and Uwe D. Hanebeck},
  title     = {{Performances Comparison of Nonlinear Filters for Indoor WLAN Positioning}},
  booktitle = {Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)},
  year      = {2008},
  address   = {Cologne, Germany},
  month     = jul,
  pages     = {1--7},
  abstract  = {Indoor WLAN positioning should be modeled as a nonlinear
               and non-Gaussian dynamic system due to the complex indoor environment,
               radio propagation and motion behaviour. The aim of this paper is to
               analyze different filtering strategies for real life indoor WLAN
               positioning systems. The performance criteria for the comparison are
               the mean of localization errors and computational complexity.
               Three nonlinear filters are analyzed: Fourier density approximation (FF),
               particle filter (PF) and grid-based filter (GF), which are representatives for
               deterministic and random density approximation approaches.
               Our experimental results help to choose the appropriate filtering
               techniques under different resource limitations.},
  pdf       = {Fusion08_Wang.pdf},
  timestamp = {2008.05.05}
}

@inproceedings{ACC08_DeisenrothPeters-GPDPoriginal,
  author    = {Marc P. Deisenroth and Jan Peters and Carl E. Rasmussen},
  title     = {{A}pproximate {Dynamic Programming with Gaussian Processes}},
  booktitle = {Proceedings of the 2008 American Control Conference (ACC 2008)},
  year      = {2008},
  pages     = {4480--4485},
  address   = {Seattle, Washington, USA},
  month     = jun,
  abstract  = {In general, it is difficult to determine an optimal closed-loop policy
               in nonlinear control problems with continuous-valued state and control
               domains. Hence, approximations are often inevitable. The standard
               method of discretizing states and controls suffers from the curse
               of dimensionality and strongly depends on the chosen temporal sampling
               rate. In this paper, we introduce Gaussian process dynamic programming
               (GPDP) and determine an approximate globally optimal closed-loop
               policy. In GPDP, value functions in the Bellman recursion of the
               dynamic programming algorithm are modeled using Gaussian processes.
               GPDP returns an optimal state-feedback for a finite set of states.
               Based on these outcomes, we learn a possibly discontinuous closed-loop
               policy on the entire state space by switching between two independently
               trained Gaussian processes. A binary classifier selects one Gaussian
               process to predict the optimal control signal. We show that GPDP
               is able to yield an almost optimal solution to an LQ problem using
               few sample points. Moreover, we successfully apply GPDP to the underpowered
               pendulum swing up, a complex nonlinear control problem.},
  pdf       = {ACC08_DeisenrothPeters-GPDPoriginal.pdf}
}

@inproceedings{ESANN08_DeisenrothRasmussen-GPDPsysid,
  author    = {Marc P. Deisenroth and Carl E. Rasmussen and Jan Peters},
  title     = {{M}odel-{Based Reinforcement Learning with Continuous States and Actions}},
  booktitle = {Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2008)},
  year      = {2008},
  pages     = {19--24},
  address   = {Bruges, Belgium},
  month     = apr,
  abstract  = {Finding an optimal policy in a reinforcement learning (RL) framework
               with continuous state and action spaces is challenging. Approximate
               solutions are often inevitable. GPDP is an approximate dynamic programming
               algorithm based on Gaussian process (GP) models for the value functions.
               In this paper, we extend GPDP to the case of unknown transition dynamics.
               After building a GP model for the transition dynamics, we apply GPDP
               to this model and determine a continuous-valued policy in the entire
               state space. We apply the resulting controller to the underpowered
               pendulum swing up. Moreover, we compare our results on this RL task
               to a nearly optimal discrete DP solution in a fully known environment.},
  pdf       = {ESANN08_DeisenrothRasmussen-GPDPsysid.pdf}
}

@incollection{Springer08_Lenz,
  booktitle = {{S}ignal {P}rocessing {T}echniques for {K}nowledge {E}xtraction and {I}nformation {F}usion},
  author    = {Henning Lenz and Bruno Betoni Parodi and Hui Wang and Andrei Szabo and Joachim Bamberger and Dragan Obradovic and Joachim Horn and Uwe D. Hanebeck},
  title     = {Adaptive Localization in Wireless Networks},
  editor    = {Danilo Mandic and Martin Golz and Anthony Luh and Dragan Obradovic and Toshihisa Tanaka},
  pages     = {97 -- 120},
  publisher = {Springer US},
  year      = {2008},
  abstract  = {Indoor positioning approaches based on communication systems typically use the received signal
               strength (RSS) as measurements. To work properly, such a system often requires many
               calibration points before its start. Based on theoretical-propagation models (RF planning)
               and on self-organizing maps (SOM) an adaptive approach for Simultaneous Localization and Learning (SLL)
               has been developed. The algorithm extracts out of online measurements the true model of the RF propagation.
               Applying SLL, a self-calibrating RSS-based positioning system with high accuracies can be realized
               without the need of cost intensive calibration measurements during system installation or
               maintenance. The main aspects of SLL are addressed as well as convergence and statistical
               properties. Results for real-world DECT and WLAN setups are given, showing that the localization
               starts with a basic performance slightly better than Cell-ID, finally reaching the accuracy of
               pattern matching using calibration points.},
  pdf       = {Fusion08_Lenz.pdf},
  doi       = {10.1007/978-0-387-74367-7_6},
  url       = {https://dx.doi.org/10.1007/978-0-387-74367-7_6}
}

@incollection{LNEE_ICINCO07_SawoRoberts,
  author    = {Felix Sawo and Kathrin Roberts and Uwe D. Hanebeck},
  title     = {{Model-Based Reconstruction of Distributed Phenomena Using Discretized Representations of Partial Differential Equations}},
  booktitle = {Informatics in Control, Automation and Robotics, Selected Papers from ICINCO 2006, Series: Lecture Notes in Electrical Engineering},
  publisher = {Springer},
  year      = {2008},
  volume    = {15},
  abstract  = {This article addresses the model-based reconstruction and prediction of distributed phenomena
               characterized by partial differential equations, which are monitored by sensor networks. The novelty of the
               proposed reconstruction method is the systematic approach and the integrated treatment of uncertainties,
               which occur in the physical model and arise naturally from noisy measurements. By this means it is possible
               not only to reconstruct the entire phenomenon, even at non-measurement points, but also to reconstruct the
               complete density function of the state characterizing the distributed phenomenon. In the first step, the
               partial differential equation, i.e., distributed-parameter system, is spatially and temporally decomposed
               leading to a finite-dimensional state space form. In the next step, the state of the resulting lumped-parameter
               system, which provides an approximation of the solution of the underlying partial differential equation,
               is dynamically estimated under consideration of uncertainties. By using the estimation results, several
               additional tasks can be achieved by the sensor network, e.g. optimal sensor placement, optimal scheduling,
               model improvement, and system identification. The performance of the proposed model-based reconstruction
               method is demonstrated by means of simulations.},
  url       = {https://dx.doi.org/10.1007/978-3-540-79142-3_24}
}

@incollection{EWRL08RasmussenDeisenroth,
  title     = {Probabilistic {Inference for Fast Learning in Control}},
  pages     = {229--242},
  publisher = {Springer-Verlag},
  year      = {2008},
  editor    = {S. Girgin and M. Loth and R. Munos and P. Preux and D. Ryabko},
  author    = {Carl E. Rasmussen and Marc P. Deisenroth},
  volume    = {5323},
  series    = {Lecture Notes in Computer Science},
  month     = nov,
  abstract  = {We provide a novel framework for very fast model-based reinforcement
               learning in continuous state and action spaces. The framework requires
               probabilistic models that explicitly characterize their levels of confidence.
               Within this framework, we use flexible, non-parametric models to describe the
               world based on previously collected experience. We demonstrate learning on the
               cart-pole problem in a setting where we provide very limited prior knowledge
               about the task. Learning progresses rapidly, and a good policy is found after
               only a hand-full of iterations.},
  booktitle = {Recent {Advances in Reinforcement Learning}. Proceedings of the 8th European Workshop on Reinforcement Learning (EWRL 2008)},
  pdf       = {EWRL08_RasmussenDeisenroth.pdf},
  timestamp = {2008.11.04}
}

@inproceedings{CDC07_HanebeckSchrempf,
  author    = {Uwe D. Hanebeck and Oliver C. Schrempf},
  title     = {{G}reedy {A}lgorithms for {D}irac {M}ixture {A}pproximation of {A}rbitrary {P}robability {D}ensity {F}unctions},
  booktitle = {Proceedings of the 2007 IEEE Conference on Decision and Control (CDC 2007)},
  year      = {2007},
  pages     = {3065--3071},
  address   = {New Orleans, Louisiana, USA},
  month     = dec,
  abstract  = {Greedy procedures for suboptimal Dirac mixture approximation of an
               arbitrary probability density function are proposed, which approach
               the desired density by sequentially adding one component at a time.
               Similar to the batch solutions proposed earlier, a distance measure
               between the corresponding cumulative distributions is minimized by
               selecting the corresponding density parameters. This is due to the
               fact, that a distance between the densities is typically not well
               defined for Dirac mixtures. This paper focuses on the Cramer-von
               Mises distance, a weighted integral quadratic distance measure between
               the true distribution and its approximation. In contrast to the batch
               solutions, the computational complexity is much lower and grows only
               linearly with the number of components. Computational savings are
               even greater, when the required number of components, e.g., the minimum
               number of components for achieving a given quality measure, is not
               a priori known and must be searched for as well. The performance
               of the proposed sequential approximation approach is compared to
               that of the optimal batch solution.},
  pdf       = {CDC07_HanebeckSchrempf.pdf},
  timestamp = {2007.07.30}
}

@inproceedings{CDC07_WeisselHuber,
  author    = {Florian Weissel and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{A} {N}onlinear {M}odel {P}redictive {C}ontrol {F}ramework {A}pproximating {N}oise {C}orrupted {S}ystems with {H}ybrid {T}ransition {D}ensities},
  booktitle = {Proceedings of the 2007 IEEE Conference on Decision and Control (CDC 2007)},
  year      = {2007},
  pages     = {3661--3666},
  address   = {New Orleans, Louisiana, USA},
  month     = dec,
  abstract  = {In this paper, a framework for Nonlinear Model Predictive Control
               (NMPC) for heavily noise-affected systems is presented. Within this
               framework, the noise influence, which originates from uncertainties
               during model identification or measurement, is explicitly considered.
               This leads to a significant increase in the control quality. One
               part of the proposed framework is the efficient state prediction,
               which is necessary for NMPC. It is based on transition density approximation
               by hybrid transition densities, which allows efficient closed-form
               state prediction of time-variant nonlinear systems with continuous
               state spaces in discrete time. Another part of the framework is a
               versatile value function representation using Gaussian mixtures,
               Dirac mixtures, and even a combination of both. Based on these methods,
               an efficient closed-form algorithm for calculating an approximate
               value function of the NMPC optimal control problem employing dynamic
               programming is presented. Thus, also very long optimization horizons
               can be used and furthermore it is possible to calculate the value
               function off-line, which reduces the on-line computational burden
               significantly. The capabilities of the framework and especially the
               benefits that can be gained by incorporating the noise in the controller
               are illustrated by the example of a miniature walking robot following
               a given path.},
  pdf       = {CDC07_WeisselHuber.pdf},
  timestamp = {2007.07.30}
}

@inproceedings{IROS07_SchrempfAlbrecht,
  author    = {Oliver C. Schrempf and David Albrecht and Uwe D. Hanebeck},
  title     = {{T}ractable {P}robabilistic {M}odels for {I}ntention {R}ecognition {B}ased on {E}xpert {K}nowledge},
  booktitle = {Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007)},
  year      = {2007},
  pages     = {1429--1434},
  address   = {San Diego, California, USA},
  month     = nov,
  abstract  = {Intention recognition is an important topic in human-robot cooperation
               that can be tackled using probabilistic model-based methods. A popular
               instance of such methods are Bayesian networks where the dependencies
               between random variables are modeled by means of a directed graph.
               Bayesian networks are very efficient for treating networks with conditionally
               independent parts. Unfortunately, such independence sometimes has
               to be constructed by introducing so called hidden variables with
               an intractably large state space. An example are human actions which
               depend on human intentions and on other human actions. Our goal in
               this paper is to find models for intention-action mapping with a
               reduced state space in order to allow for tractable on-line evaluation.
               We present a systematic derivation of the reduced model and experimental
               results of recognizing the intention of a real human in a virtual
               environment.},
  pdf       = {IROS07_SchrempfAlbrecht.pdf}
}

@inproceedings{IROS07_WeisselHuber,
  author    = {Florian Weissel and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{T}est-{E}nvironment based on a {T}eam of {M}iniature {W}alking {R}obots for {E}valuation of {C}ollaborative {C}ontrol {M}ethods},
  booktitle = {Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007)},
  year      = {2007},
  pages     = {2474--2479},
  address   = {San Diego, California, USA},
  month     = nov,
  abstract  = {For the collaborative control of a team of robots, a set of well-suited
               high-level control algorithms, especially for path planning and measurement
               scheduling, is essential. The quality of these control algorithms
               can be significantly increased by considering uncertainties that
               arise, e.g. from noisy measurements or system model abstraction,
               by incorporating stochastic filters into the control. To develop
               these kinds of algorithms and to prove their effectiveness, obviously
               real-world experiments with real world uncertainties are mandatory.
               Therefore, a test-environment for evaluating algorithms for collaborative
               control of a team of robots is presented. This test-environment is
               founded on miniature walking robots with six degrees of freedom.
               Their novel locomotion concept not only allows them to move in a
               wide variety of different motion patterns far beyond the possibilities
               of traditionally employed wheel-based robots, but also to handle
               real-world conditions like uneven ground or small obstacles. These
               robots are embedded in a modular test-environment, comprising infrastructure
               and simulation modules as well as a high-level control module with
               submodules for pose estimation, path planning, and measurement scheduling.
               The interaction of the individual modules of the introduced test-environment
               is illustrated by an experiment from the field of cooperative localization
               with focus on measurement scheduling, where the robots that perform
               distance measurements are selected based on a novel criterion, the
               normalized mutual Mahalanobis distance.},
  pdf       = {IROS07_WeisselHuber.pdf}
}

@inproceedings{SDF07_Huber,
  author    = {Marco F. Huber and Eric Stiegeler and Uwe D. Hanebeck},
  title     = {{O}n {S}ensor {S}cheduling in {C}ase of {U}nreliable {C}ommunication},
  booktitle = {INFORMATIK 2007 - the 37th Annual Conference of the Gesellschaft f{\"u}r Informatik e.V. (GI), 3rd German Workshop Sensor Data Fusion: Trends, Solutions, Applications (SDF 2007)},
  year      = {2007},
  pages     = {90--94},
  address   = {Bremen, Germany},
  month     = sep,
  abstract  = {This paper deals with the linear discrete-time sensor scheduling problem
               in unreliable communication networks. In case of the common assumption
               of an error-free communication, the sensor scheduling problem, where
               one sensor from a sensor network is selected for measuring at a specific
               time instant so that the estimation errors are minimized, can be
               solved off-line by extensive tree search. For the more realistic
               scenario, where communication is unreliable, a scheduling approach
               using a prioritization list for the sensors is proposed that leads
               to a minimization of the estimation error by selecting the most beneficial
               sensor on-line. To lower the computational demand for the priority
               list calculation, a novel optimal pruning approach is introduced.},
  keywords  = {Sensor Scheduling},
  pdf       = {SDF07_Huber.pdf},
  timestamp = {2007.06.18}
}

@inproceedings{ACC07_Huber,
  author    = {Marco F. Huber and Dietrich Brunn and Uwe D. Hanebeck},
  title     = {{E}fficient {N}onlinear {M}easurement {U}pdating based on {G}aussian {M}ixture {A}pproximation of {C}onditional {D}ensities},
  booktitle = {Proceedings of the 2007 American Control Conference (ACC 2007)},
  year      = {2007},
  pages     = {4425--4430},
  address   = {New York, New York, USA},
  month     = jul,
  abstract  = {Filtering or measurement updating for nonlinear stochastic dynamic
               systems requires approximate calculations, since an exact solution
               is impossible to obtain in general. We propose a Gaussian mixture
               approximation of the conditional density, which allows performing
               measurement updating in closed form. The conditional density is a
               probabilistic representation of the nonlinear system and depends
               on the random variable of the measurement given the system state.
               Unlike the likelihood, the conditional density is independent of
               actual measurements, which permits determining its approximation
               off-line. By treating the approximation task as an optimization problem,
               we use progressive processing to achieve high quality results. Once
               having calculated the conditional density, the likelihood can be
               determined on-line, which, in turn, offers an efficient approximate
               filter step. As result, a Gaussian mixture representation of the
               posterior density is obtained. The exponential growth of Gaussian
               mixture components resulting from repeated filtering is avoided implicitly
               by the prediction step using the proposed techniques.},
  keywords  = {System Approximation, Gaussian Mixtures},
  pdf       = {ACC07_Huber-GMLikelihood.pdf}
}

@inproceedings{ACC07_Schrempf-DiracMixt,
  author       = {Oliver C. Schrempf and Uwe D. Hanebeck},
  booktitle    = {Proceedings of the 2007 American Control Conference (ACC 2007)},
  title        = {{R}ecursive {P}rediction of {S}tochastic {N}onlinear {S}ystems {B}ased on {O}ptimal {D}irac {M}ixture {A}pproximations},
  year         = {2007},
  address      = {New York, New York, USA},
  month        = jul,
  pages        = {1768--1774},
  abstract     = {This paper introduces a new approach to the recursive propagation
                  of probability density functions through discrete-time stochastic
                  nonlinear dynamic systems. An efficient recursive procedure is proposed
                  that is based on the optimal approximation of the posterior densities
                  after each prediction step by means of Dirac mixtures. The parameters
                  of the individual components are selected by systematically minimizing
                  a suitable distance measure in such a way that the future evolution
                  of the approximate densities is as close to the exact densities as
                  possible.},
  howpublished = {submitted},
  keywords     = {Dirac Mixtures},
  pdf          = {ACC07_Schrempf-DiracMixt.pdf}
}

@inproceedings{ACC07_WeisselHuber,
  author    = {Florian Weissel and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{E}fficient {C}ontrol of {N}onlinear {N}oise--{C}orrupted {S}ystems {U}sing a {N}ovel {M}odel {P}redictive {C}ontrol {F}ramework},
  booktitle = {Proceedings of the 2007 American Control Conference (ACC 2007)},
  year      = {2007},
  pages     = {3751--3756},
  address   = {New York, New York, USA},
  month     = jul,
  abstract  = {Model identification and measurement acquisition is always to some
               degree uncertain. Therefore, a framework for Nonlinear Model Predictive
               Control (NMPC) is proposed that explicitly considers the noise influence
               on nonlinear dynamic systems with continuous state spaces and a finite
               set of control inputs in order to significantly increase the control
               quality. Integral parts of NMPC are the prediction of system states
               over a finite horizon as well as the problem specific modeling of
               reward functions. For achieving an efficient and also accurate state
               prediction, the introduced framework uses transition densities approximated
               by means of axis-aligned Gaussian mixtures. The representation power
               of Gaussian mixtures is also used to model versatile reward functions.
               Thus, together with the prediction technique a closed-form calculation
               of the optimization problems arising from NMPC is possible. Additionally,
               an efficient algorithm for calculating an approximate value function
               of the corresponding optimal control problem employing dynamic programming
               is presented. Thus, the value function can be calculated off-line,
               which reduces the on-line computational burden significantly and
               also permits the use of long optimization horizons. The capabilities
               of the framework and especially the benefits that can be gained by
               incorporating the noise in the controller are illustrated by the
               example of a two-wheeled differential-drive mobile robot following
               a given path.},
  pdf       = {ACC07_WeisselHuber.pdf}
}

@inproceedings{ECC07_DeisenrothWeissel,
  author    = {Marc P. Deisenroth and Florian Weissel and Toshiyuki Ohtsuka and Uwe D. Hanebeck},
  title     = {{Online-Computation Approach to Optimal Control of Noise-Affected Nonlinear Systems with Continuous State and Control Spaces}},
  booktitle = {Proceedings of the 2007 European Control Conference (ECC 2007)},
  year      = {2007},
  address   = {Kos, Greece},
  month     = jul,
  abstract  = {A novel online-computation approach to optimal control of nonlinear,
               noise-affected systems with continuous state and control spaces is
               presented. In the proposed algorithm, system noise is explicitly
               incorporated into the control decision. This leads to superior results
               compared to state-of-the-art nonlinear controllers that neglect this
               influence. The solution of an optimal nonlinear controller for a
               corresponding deterministic system is employed to find a meaningful
               state space restriction. This restriction is obtained by means of
               approximate state prediction using the noisy system equation. Within
               this constrained state space, an optimal closed-loop solution for
               a finite decisionmaking horizon (prediction horizon) is determined
               within an adaptively restricted optimization space. Interleaving
               stochastic dynamic programming and value function approximation yields
               a solution to the considered optimal control problem. The enhanced
               performance of the proposed discrete-time controller is illustrated
               by means of a scalar example system. Nonlinear model predictive control
               is applied to address approximate treatment of infinite-horizon problems
               by the finite-horizon controller.},
  pdf       = {ECC07_DeisenrothWeissel.pdf}
}

@inproceedings{Fusion07_HanselmannSchrempf,
  author    = {Anne Hanselmann and Oliver C. Schrempf and Uwe D. Hanebeck},
  title     = {{O}ptimal {P}arametric {D}ensity {E}stimation by {M}inimizing an {A}nalytic {D}istance {M}easure},
  booktitle = {Proceedings of the 10th International Conference on Information Fusion (Fusion 2007)},
  year      = {2007},
  address   = {Québec, Canada},
  month     = jul,
  abstract  = {In this paper, we present a novel approach to parametric density estimation
               from given samples. The samples are treated as a parametric density
               function by means of a Dirac mixture, which allows for applying analytic
               optimization techniques. The method is based on minimizing a distance
               measure between the integral of the approximation function and the
               empirical cumulative distribution function (EDF) of the given samples,
               where the EDF is represented by the integral of the Dirac mixture.
               Since this minimization problem cannot be solved directly in general,
               a progression technique is applied. Increased performance of the
               approach in comparison to iterative maximum likelihood approaches
               is shown in simulations.},
  pdf       = {Fusion07_HanselmannSchrempf.pdf}
}

@inproceedings{Fusion07_Huber,
  author    = {Marco F. Huber and Uwe D. Hanebeck},
  title     = {{T}he {H}ybrid {D}ensity {F}ilter for {N}onlinear {E}stimation based on {H}ybrid {C}onditional {D}ensity {A}pproximation},
  booktitle = {Proceedings of the 10th International Conference on Information Fusion (Fusion 2007)},
  year      = {2007},
  address   = {Québec, Canada},
  month     = jul,
  abstract  = {In nonlinear Bayesian estimation it is generally inevitable to incorporate
               approximate descriptions of the exact estimation algorithm. There
               are two possible ways to involve approximations: Approximating the
               nonlinear stochastic system model or approximating the prior probability
               density function. The key idea of the introduced novel estimator
               called Hybrid Density Filter relies on approximating the nonlinear
               system, thus approximating conditional densities. These densities
               nonlinearly relate the current system state to the future system
               state at predictions or to potential measurements at measurement
               updates. A hybrid density consisting of both Dirac delta functions
               and Gaussian densities is used for an optimal approximation. This
               paper addresses the optimization problem for treating the conditional
               density approximation. Furthermore, efficient estimation algorithms
               are derived based upon the special structure of the hybrid density,
               which yield a Gaussian mixture representation of the system state's
               density.},
  keywords  = {System Approximation, Hybrid Density},
  pdf       = {Fusion07_Huber.pdf}
}

@inproceedings{Fusion07_SawoHuber-ParamIdent,
  author    = {Felix Sawo and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{P}arameter {I}dentification and {R}econstruction for {D}istributed {P}henomena {B}ased on {H}ybrid {D}ensity {F}ilter},
  booktitle = {Proceedings of the 10th International Conference on Information Fusion (Fusion 2007)},
  year      = {2007},
  address   = {Québec, Canada},
  month     = jul,
  abstract  = {This paper addresses the problem of model-based reconstruction and
               parameter identification of distributed phenomena characterized by
               partial differential equations. The novelty of the proposed method
               is the systematic approach and the integrated treatment of uncertainties,
               which naturally occur in the physical system and arise from noisy
               measurements. The main challenge of accurate reconstruction is that
               model parameters, i.e., diffusion coefficients, of the physical model
               are not known in advance and usually need to be identified. Generally,
               the problem of parameter identification leads to a nonlinear estimation
               problem. Hence, a novel efficient recursive procedure is employed.
               Unlike other estimators, the so-called Hybrid Density Filter not
               only assures accurate estimation results for nonlinear systems, but
               also offers an efficient processing. By this means it is possible
               to reconstruct and identify distributed phenomena monitored by autonomous
               wireless sensor networks. The performance of the proposed estimation
               method is demonstrated by means of simulations.},
  pdf       = {Fusion07_SawoHuber-ParamIdent.pdf}
}

@inproceedings{HCI07_Wang,
  author    = {Hui Wang and Henning Lenz and Andrei Szabo and Joachim Bamberger and Uwe D. Hanebeck},
  title     = {{E}nhancing the {M}ap {U}sage for {I}ndoor {L}ocation-{A}ware {S}ystems},
  booktitle = {International Conference on Human-Computer Interaction (HCI 2007)},
  year      = {2007},
  address   = {Peking, China},
  month     = jul,
  abstract  = {Location-aware systems are receiving more and more interest in both
               academia and industry due to their promising prospective in a broad
               category of so-called Location-Based-Services (LBS). The map interface
               plays a crucial role in the location-aware systems, especially for
               indoor scenarios. This paper addresses the usage of map information
               in a Wireless LAN (WLAN)-based indoor navigation system. We describe
               the benefit of using maNMp information in multiple algorithms of
               the system, including radio-map generation, tracking, semantic positioning
               and navigation. Then we discuss how to represent or model the indoor
               map to fulfill the requirements of intelligent algorithms. We believe
               that a vector-based multi-layer representation is the best choice
               for indoor location-aware system.},
  pdf       = {HCI07_Wang.pdf}
}

@inproceedings{CARS07_Roberts,
  author    = {Kathrin Roberts and Uwe D. Hanebeck},
  title     = {{M}otion {E}stimation and {R}econstruction of a {H}eart {S}urface by {M}eans of 2{D}-/3{D}- {M}embrane {M}odels},
  booktitle = {Proceedings of 21st International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS 2007)},
  year      = {2007},
  pages     = {243--245},
  address   = {Berlin, Germany},
  month     = jun,
  abstract  = {In order to assist surgeons during minimally invasive interventions
               on the beating heart, it would be helpful to develop a robotic surgery
               system, which synchronizes the instruments with the heart surface,
               so that their positions do not change relative to the point of interest
               (POI). The synchronization of the robotic manipulators requires an
               estimation of the heart surface motion. In this paper, a modelbased
               motion estimation of the heart surface is presented. The motion of
               a partition of the heart surface is modelled by means of a thin or
               thick vibrating membrane in order to represent the epicardial surface
               or the connected epicard and myocard. The membrane motion is described
               by means of a system of coupled linear partial differential equations
               (PDEs), whose 3D-input function is assumed to be known. After spatial
               discretization of the PDE solution space by the Finite Spectral Element
               Method, a bank of lumped systems is obtained. A Kalman filter is
               used to estimate the state of the lumped systems by incorporating
               noisy measurements of the heart surface. Measurements can be the
               position or velocity of sonomicrometry-based sensors or of certain
               landmarks, which are tracked by optical sensors. With the model-based
               estimation it is possible to reconstruct the entire partition of
               the heart surface even at non-measurement points and thus at each
               POI.},
  pdf       = {CARS07_Roberts.pdf}
}

@inproceedings{ICINCO07_Schrempf-DiracMixt,
  author    = {Oliver C. Schrempf and Uwe D. Hanebeck},
  title     = {{A} {S}tate {E}stimator for {N}onlinear {S}tochastic {S}ystems {B}ased on {D}irac {M}ixture {A}pproximations},
  booktitle = {Proceedings of the 4th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2007)},
  year      = {2007},
  volume    = {SPSMC},
  pages     = {54--61},
  address   = {Angers, France},
  month     = may,
  abstract  = {This paper presents a filter approach for estimating the state of
               nonlinear dynamic systems based on recursive approximation of posterior
               densities by means of Dirac mixture functions. The filter consists
               of a prediction step and a filter step. The approximation approach
               is based on a systematic minimization of a distance measure and is
               hence optimal and deterministic. In contrast to non-deterministic
               methods we are able to determine the optimal number of components
               in the Dirac mixture. A further benefit of the proposed approach
               is the consideration of measurements during the approximation process
               in order to avoid parameter degradation.},
  keywords  = {Dirac Mixtures},
  pdf       = {ICINCO07_Schrempf-DiracMixt.pdf},
  timestamp = {2007.05.30}
}

@inproceedings{ICINCO07_WeisselHuber,
  author    = {Florian Weissel and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{A} {C}losed--{F}orm {M}odel {P}redictive {C}ontrol {F}ramework for {N}onlinear {N}oise--{C}orrupted {S}ystems},
  booktitle = {Proceedings of the 4th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2007)},
  year      = {2007},
  volume    = {SPSMC},
  pages     = {62--69},
  address   = {Angers, France},
  month     = may,
  abstract  = {In this paper, a framework for Nonlinear Model Predictive Control
               (NMPC) that explicitly incorporates the noise influence on systems
               with continuous state spaces is introduced. By the incorporation
               of noise, which results from uncertainties during model identification
               and the measurement process, the quality of control can be significantly
               increased. Since NMPC requires the prediction of system states over
               a certain horizon, an efficient state prediction technique for nonlinear
               noise-affected systems is required. This is achieved by using transition
               densities approximated by axis-aligned Gaussian mixtures together
               with methods to reduce the computational burden. A versatile cost
               function representation also employing Gaussian mixtures provides
               an increased freedom of modeling. Combining the prediction technique
               with this value function representation allows closed-form calculation
               of the necessary optimization problems arising from NMPC. The capabilities
               of the framework and especially the benefits that can be gained by
               considering the noise in the controller are illustrated by the example
               of a mobile robot following a given path.},
  pdf       = {ICINCO07_WeisselHuber.pdf}
}

@inproceedings{ICRA07_Bader,
  author    = {Thomas Bader and Alexander Wiedemann and Kathrin Roberts and Uwe D. Hanebeck},
  title     = {{M}odel--based {M}otion {E}stimation of {E}lastic {S}urfaces for {M}inimally {I}nvasive {C}ardiac {S}urgery},
  booktitle = {Proceedings of the 2007 IEEE International Conference on Robotics and Automation (ICRA 2007)},
  year      = {2007},
  pages     = {2261--2266},
  address   = {Rome, Italy},
  month     = apr,
  abstract  = {In order to assist surgeons during surgery on moving organs, e.g.
               minimally invasive beating heart bypass surgery, a master-slave system
               which synchronizes surgical instruments with the organ's motion is
               desired. This synchronization requires reliable estimation of the
               organ's motion. In this paper, we present a new approach to motion
               estimation based on a state motion model for a partition of the heart's
               surface. Its motion behavior is described by a partial differential
               equation whose input function is assumed to be periodic. An estimator
               is used on one hand to predict future model states based on reconstruction
               of the input function and on the other hand to incorporate noisy
               spatially discrete measurements in order to improve state estimation.
               The model-based motion estimation is evaluated using a simple heart
               simulator. Measurements are obtained by reconstructing 3D position
               of markers on a pulsating membrane by means of a stereo camera system.},
  doi       = {10.1109/ROBOT.2007.363656},
  pdf       = {ICRA07_Bader.pdf}
}

@inproceedings{IPSN07_Huber,
  author    = {Marco F. Huber and Uwe D. Hanebeck},
  title     = {{H}ybrid {T}ransition {D}ensity {A}pproximation for {E}fficient {R}ecursive {P}rediction of {N}onlinear {D}ynamic {S}ystems},
  booktitle = {International Conference on Information Processing in Sensor Networks (IPSN 2007)},
  year      = {2007},
  pages     = {283--292},
  address   = {Cambridge, Massachusetts, USA},
  month     = apr,
  abstract  = {For several tasks in sensor networks, such as localization, information
               fusion, or sensor scheduling, Bayesian estimation is of paramount
               importance. Due to the limited computational and memory resources
               of the nodes in a sensor network, evaluation of the prediction step
               of the Bayesian estimator has to be performed very efficiently. An
               exact and closed-form representation of the predicted probability
               density function of the system state is typically impossible to obtain,
               since exactly solving the prediction step for nonlinear discrete-time
               dynamic systems in closed form is unfeasible. Assuming additive noise,
               we propose an accurate approximation of the predicted density, that
               can be calculated efficiently by optimally approximating the transition
               density using a hybrid density. A hybrid density consists of two
               different density types: Dirac delta functions that cover the domain
               of the current density of the system state, and another density type,
               e.g. Gaussian densities, that cover the domain of the predicted density.
               The freely selectable, second density type of the hybrid density
               depends strongly on the noise affecting the nonlinear system. So,
               the proposed approximation framework for nonlinear prediction is
               not restricted to a specific noise density. It further allows an
               analytical evaluation of the Chapman-Kolmogorov prediction equation
               and can be interpreted as a deterministic sampling estimation approach.
               In contrast to methods using random sampling like particle filters,
               a dramatic reduction in the number of components and a subsequent
               decrease in computation time for approximating the predicted density
               is gained.},
  keywords  = {System Approximation, Hybrid Density},
  pdf       = {IPSN2007_Huber.pdf}
}

@article{ITM07_BrunnSawo,
  author    = {Dietrich Brunn and Felix Sawo and Uwe D. Hanebeck},
  title     = {{M}odellbasierte {V}ermessung verteilter {P}h{\"a}nomene und {G}enerierung optimaler {M}esssequenzen},
  journal   = {tm -- Technisches Messen, Oldenbourg Verlag},
  year      = {2007},
  volume    = {3},
  pages     = {75--90},
  month     = mar,
  abstract  = {Dieser Beitrag befasst sich mit modellbasierten Methoden zur Vermessung
               verteilter physikalischer Ph{\"a}nomene. Diese Methoden zeichnen
               sich durch eine systematische Behandlung von Unsicherheiten aus,
               so dass neben der Rekonstruktion der vollst{\"a}ndigen Wahrscheinlichkeitsdichte
               der relevanten Gr{\"o}\ss{}en aus einer geringen Anzahl von zeit-,
               orts- und wertdiskreten Messungen auch die Generierung optimaler
               Messsequenzen m{\"o}glich ist. Es wird dargestellt, wie eine Beschreibung
               f{\"u}r ein verteilt"=parametrisches System in Form einer partiellen
               Differentialgleichung, welche einen unendlich-dimensionalen Zustandsraum
               beschreibt, in eine konzentriert-parametrische Form konvertiert wird.
               Diese kann als Grundlage f{\"u}r den Entwurf klassischer Sch{\"a}tzer,
               wie z. B. des Kalman-Filters, dienen. Ferner wird eine Methode zur
               Sensoreinsatzplanung vorgestellt, mit der eine optimale Sequenz von
               Messparametern bestimmt werden kann, um mit einem minimalen Messaufwand
               die Unsicherheit auf ein gew{\"u}nschtes Ma\ss{} zu reduzieren. Die
               Anwendung dieser Methoden wird an zwei Beispielen, einer Temperaturverteilung
               und der Verformung einer F{\"u}hrungsschiene, demonstriert. Zus{\"a}tzlich
               werden die Herausforderungen bei der Behandlung nichtlinearer Systeme
               und die Probleme bei der dezentralen Verarbeitung, wie sie typischerweise
               beim Einsatz von Sensornetzwerken auftreten, diskutiert.},
  booktitle = {Informationsfusion in der Mess- und Sensortechnik},
  editor    = {J{\"u}rgen Beyerer and Fernando {Puente Le{\'o}n} and Klaus-Dieter Sommer},
  pdf       = {ITM07_BrunnSawo.pdf},
  publisher = {Universit{\"a}tsverlag Karlsruhe}
}

@inproceedings{WPNC07_Wang,
  author    = {Hui Wang and Henning Lenz and Andrei Szabo and Joachim Bamberger and Uwe D. Hanebeck},
  title     = {{WLAN}--{B}ased {P}edestrian {T}racking {U}sing {P}article {F}ilters and {L}ow--{C}ost {MEMS} {S}ensors},
  booktitle = {Workshop on Positioning, Navigation and Communication, (WPNC 2007)},
  year      = {2007},
  address   = {Hannover, Germany},
  month     = mar,
  pdf       = {WPNC07_Wang.pdf},
  abstract  = {Indoor positioning systems based on Wireless LAN
               (WLAN) are being widely investigated in academia and industry.
               Meanwhile, the emerging low-cost MEMS sensors can also be used
               as another independent positioning source. In this paper, we
               propose a pedestrian tracking framework based on particle filters,
               which extends the typical WLAN-based indoor positioning systems
               by integrating low-cost MEMS accelerometer and map
               information. Our simulation and real world experiments indicate a
               remarkable performance improvement by using this fusion
               framework.}
}

@inproceedings{CDC06_Brunn-Fourier,
  author    = {Dietrich Brunn and Felix Sawo and Uwe D. Hanebeck},
  title     = {{N}onlinear {M}ultidimensional {B}ayesian {E}stimation with {F}ourier {D}ensities},
  booktitle = {Proceedings of the 2006 IEEE Conference on Decision and Control (CDC 2006)},
  year      = {2006},
  pages     = {1303--1308},
  address   = {San Diego, California, USA},
  month     = dec,
  abstract  = {Efficiently implementing nonlinear Bayesian estimators is still an
               unsolved problem, especially for the multidimensional case. A trade-off
               between estimation quality and demand on computational resources
               has to be found. Using multidimensional Fourier series as representation
               for probability density functions, so called Fourier densities, is
               proposed. To ensure non-negativity, the approximation is performed
               indirectly via Psi-densities, of which the absolute square represent
               the Fourier density. It is shown that PSI-densities can be determined
               using the efficient fast Fourier transform algorithm and their coefficients
               have an ordering with respect to the Hellinger metric. Furthermore,
               the multidimensional Bayesian estimator based on Fourier Densities
               is derived in closed form. That allows an efficient realization of
               the Bayesian estimator where the demands on computational resources
               are adjustable.},
  pdf       = {CDC06_Brunn-Fourier.pdf}
}

@inproceedings{CDC06_Schrempf-DiracMixt,
  author    = {Oliver C. Schrempf and Dietrich Brunn and Uwe D. Hanebeck},
  title     = {{D}ensity {A}pproximation {B}ased on {D}irac {M}ixtures with {R}egard to {N}onlinear {E}stimation and {F}iltering},
  booktitle = {Proceedings of the 2006 IEEE Conference on Decision and Control (CDC 2006)},
  year      = {2006},
  address   = {San Diego, California, USA},
  month     = dec,
  abstract  = {A deterministic procedure for optimal approximation of arbitrary probability
               density functions by means of Dirac mixtures with equal weights is
               proposed. The optimality of this approximation is guaranteed by minimizing
               the distance of the approximation from the true density. For this
               purpose a distance measure is required, which is in general not well
               defined for Dirac mixtures. Hence, a key contribution is to compare
               the corresponding cumulative distribution functions. This paper concentrates
               on the simple and intuitive integral quadratic distance measure.
               For the special case of a Dirac mixture with equally weighted components,
               closed-form solutions for special types of densities like uniform
               and Gaussian densities are obtained. Closed-form solution of the
               given optimization problem is not possible in general. Hence, another
               key contribution is an efficient solution procedure for arbitrary
               true densities based on a homotopy continuation approach. In contrast
               to standard Monte Carlo techniques like particle filters that are
               based on random sampling, the proposed approach is deterministic
               and ensures an optimal approximation with respect to a given distance
               measure. In addition, the number of required components (particles)
               can easily be deduced by application of the proposed distance measure.
               The resulting approximations can be used as basis for recursive nonlinear
               filtering mechanism alternative to Monte Carlo methods.},
  keywords  = {Dirac Mixtures},
  pdf       = {CDC06_Schrempf-DiracMixt.pdf}
}

@inproceedings{CCA06_Sawo-ParameterizedGM,
  author    = {Felix Sawo and Dietrich Brunn and Uwe D. Hanebeck},
  title     = {{P}arameterized {J}oint {D}ensities with {G}aussian {M}ixture {M}arginals and their {P}otential {U}se in {N}onlinear {R}obust {E}stimation},
  booktitle = {Proceedings of the 2006 IEEE International Conference on Control Applications (CCA 2006)},
  year      = {2006},
  address   = {Munich, Germany},
  month     = oct,
  abstract  = {This paper addresses the challenges of the fusion of two random vectors
               with imprecisely known stochastic dependency. This problem mainly
               occurs in decentralized estimation, e.g. of a distributed phenomenon,
               where the stochastic dependencies between the individual states are
               not stored. To cope with such problems we propose to exploit parameterized
               joint densities with both Gaussian marginals and Gaussian mixture
               marginals. Under structural assumptions these parameterized joint
               densities contain all information about the stochastic dependencies
               between their marginal densities in terms of a generalized correlation
               parameter vector xi. The parameterized joint densities are applied
               to the prediction step and the measurement step under imprecisely
               known correlation leading to a whole family of possible estimation
               results. The resulting density functions are characterized by the
               generalized correlation parameter vector xi. Once this structure
               and the bounds of these parameters are known, it is possible to find
               bounding densities containing all possible density functions, i.e.,
               conservative estimation results.},
  pdf       = {CCA06_Sawo-ParameterizedGM.pdf}
}

@inproceedings{HCRS06_RoesslerSchrempf,
  author    = {Patrick R{\"o}\ss{}ler and Oliver C. Schrempf and Uwe D. Hanebeck},
  title     = {{S}tochastic {P}rediction of {W}aypoints for {E}xtended-{R}ange {T}elepresence {A}pplications},
  booktitle = {2nd International Workshop on Human Centered Robotic Systems (HCRS 2006)},
  year      = {2006},
  pages     = {85--89},
  address   = {Munich, Germany},
  month     = oct,
  pdf       = {HCRS06_RoesslerSchrempf.pdf},
  abstract  = {The Motion Compression framework for extended range telepresence
               applications consists of three functional modules:
               path prediction, path transformation, and user guidance. This
               paper presents a new path prediction module for known environments
               that exploits the property, that humans typically
               walk on straight paths toward discrete goal objects. In order
               to estimate the user's goal object out of a set of possible goals,
               we derived a Bayesian filter that gives this discrete estimate
               based on continuous measurements of the user's head pose.}
}

@inproceedings{HCRS06_SchrempfSchmid,
  author    = {Andreas J. Schmid and Oliver C. Schrempf and Heinz W{\"o}rn and Uwe D. Hanebeck},
  title     = {{T}owards {I}ntuitive {H}uman-{R}obot {C}ooperation},
  booktitle = {2nd International Workshop on Human Centered Robotic Systems (HCRS 2006)},
  year      = {2006},
  pages     = {7--12},
  address   = {Munich, Germany},
  month     = oct,
  pdf       = {HCRS06_SchrempfSchmid.pdf},
  abstract  = {Human-robot cooperation calls for the treatment of human-machine
               communication channels, especially if humanoid robots
               are involved. In this paper, we consider implicit nonverbal
               channels given by recognizing the partner's intention
               and proactive execution of tasks. We propose a method that
               keeps the human in the loop and allows for the systematic reduction
               of uncertainty inherent in implicit cooperation. We
               present a benchmark scenario as well as preliminary implementation
               results.}
}

@inproceedings{IROS06_Roessler,
  author    = {Patrick R{\"o}\ss{}ler and Uwe D. Hanebeck},
  title     = {{S}imultaneous {M}otion {C}ompression for {M}ulti--{U}ser {E}xtended {R}ange {T}elepresence},
  booktitle = {Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2006)},
  year      = {2006},
  pages     = {5189--5194},
  address   = {Beijing, China},
  month     = oct,
  doi       = {10.1109/IROS.2006.281656},
  pdf       = {IROS06_Roessler.pdf},
  abstract  = {Extended range telepresence allows a human user
               to intuitively teleoperate a mobile robot through arbitrarily large
               remote environments by natural walking. In order to give the user
               the possibility to navigate the robot through an arbitrarily large
               remote environments, while his own environment is of limited
               size, Motion Compression is used. The Motion Compression
               framework provides a nonlinear transformation between the
               user's path and the robot's path, which preserves path length and
               turning angles. There is, however, a difference in path curvature,
               which is minimized in order to guarantee a high degree of
               immersion.
               A major drawback of the current system is its inability to
               deal with non-convex time-variant environments or environments
               shared by multiple users. This paper presents a systematic
               approach to extending Motion Compression to non-convex environments.
               This solution will then be used to cover the multi-user
               case.}
}

@incollection{IMS06_BrunnSawo,
  author    = {Dietrich Brunn and Felix Sawo and Uwe D. Hanebeck},
  title     = {{I}nformationsfusion f{\"u}r verteilte {S}ysteme},
  booktitle = {Informationsfusion in der Mess- und Sensortechnik},
  publisher = {Universit{\"a}tsverlag Karlsruhe},
  year      = {2006},
  editor    = {J{\"u}rgen Beyerer and Fernando {Puente Le{\'o}n} and Klaus-Dieter Sommer},
  pages     = {75--90},
  month     = sep,
  abstract  = {Dieser Beitrag befasst sich mit modellbasierten Methoden zur Vermessung
               verteilter physikalischer Ph{\"a}nomene. Diese Methoden zeichnen
               sich durch eine systematische Behandlung stochastischer Unsicherheiten
               aus, so dass neben der Rekonstruktion der vollst{\"a}ndigen Wahrscheinlichkeitsdichte
               der relevanten Gr{\"o}\ss{}en aus einer geringen Anzahl von zeit-,
               orts- und wertdiskreten Messungen auch die Generierung optimaler
               Messsequenzen m{\"o}glich ist. Es wird dargestellt, wie eine Beschreibung
               f{\"u}r ein verteilt"=parametrisches System in Form einer partiellen
               Differentialgleichung, welche einen unendlich"=dimensionalen Zustandsraum
               beschreibt, in eine konzentriert"=parametrische Form konvertiert
               wird. Diese kann als Grundlage f{\"u}r den Entwurf klassischer Sch{\"a}tzer,
               wie z.B. des Kalman Filters, dienen. Ferner wird eine Methode zur
               Sensoreinsatzplanung vorgestellt, mit der eine optimale Sequenz von
               Messparametern bestimmt werden kann, um mit einem minimalen Messaufwand
               die Unsicherheit auf ein gew{\"u}nschtes Ma\ss{} zu reduzieren. Die
               Anwendung dieser Methoden wird an zwei Beispielen, einer Temperaturverteilung
               und der Verformung einer F{\"u}hrungsschiene, demonstriert. Zus{\"a}tzlich
               werden die Herausforderungen bei der Behandlung nichtlinearer Systeme
               und die Probleme bei der dezentralen Verarbeitung, wie sie typischerweise
               beim Einsatz von Sensornetzwerken auftreten, diskutiert.},
  pdf       = {IMS06_BrunnSawo.pdf}
}

@inproceedings{MFI06_Beutler,
  author    = {Frederik Beutler and Uwe D. Hanebeck},
  title     = {{T}he {P}robabilistic {I}nstantaneous {M}atching {A}lgorithm},
  booktitle = {Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006)},
  year      = {2006},
  pages     = {311--316},
  address   = {Heidelberg, Germany},
  month     = sep,
  abstract  = {A new Bayesian filtering technique for estimating signal parameters
               directly from discrete-time sequences is introduced. The so called
               probabilistic instantaneous matching algorithm recursively updates
               the probability density function of the parameters for every received
               sample and, thus, provides a high update rate up to the sampling
               rate with high accuracy. In order to do so, one of the signal sequences
               is used as part of a time-variant nonlinear measurement equation.
               Furthermore, the time-variant nature of the parameters is explicitly
               considered via a system equation, which describes the evolution of
               the parameters over time. An important feature of the probabilistic
               instantaneous matching algorithm is that it provides a probability
               density function over the parameter space instead of a single point
               estimate. This probability density function can be used in further
               processing steps, e.g. a range based localization algorithm in the
               case of time-of-arrival estimation.},
  pdf       = {MFI06_Beutler.pdf}

license_type: permissive

branch_name: refs/heads/master

gha_language: TeX

extension: bib