text:
.7490807&partnerID=40&md5=b3549580bd168c5f9c2b0f6f87e4bde8},
affiliation={State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China},
abstract={The Internet of Things (IoT) has been widely applied in sensing systems, which connect various sensors and gather their information. Data fusion, which is to integrate multiple data representing the same real-world object into a consistent and accurate representation, is an important issue. Most of existing approaches of data fusion involve various analytical procedures whose high computational complexity usually requires massive computing resources. In this paper, an efficient data fusion approach is proposed. Stochastic models are presented to describe the measurements, and quantitative analyses are given to evaluate the efficacy of data fusion. Complex event processing engine is applied to improve the efficiency of data transmission and processing to meet the real-Time demands. Finally, simulation experiments are conducted to validate the efficacy of the approach. © 2015 IEEE.},
author_keywords={Complex Event Processing;  Data Fusion;  Internet of Things;  Stochastic Mathematics},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{George201676,
author={George, D.E.a  and Unnikrishnan, A.b  c },
title={On the divergence of information filter for multi sensors fusion},
journal={Information Fusion},
year={2016},
volume={27},
pages={76-84},
doi={10.1016/j.inffus.2015.05.005},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84931275320&doi=10.1016%2fj.inffus.2015.05.005&partnerID=40&md5=7ad6204ef1af6457e827c24aae6a613a},
affiliation={Cochin University of Science and Technology, Cochin, India; NPOL (Defense Research and Development Organization), India; Rajagiri School of Engineering and Technology, Cochin, India},
abstract={The paper deals with the divergence of information filter in multi sensor target tracking problem using bearing only measurements. Information filter has a number of advantages in terms of computational requirements over Kalman filter for target tracking applications. Compared to Kalman filter it also has the advantage that one can start estimation even without an initial estimate. But this filter is seen to diverge after tracking for a short period of time, even when the target is moving at a constant velocity. A technique to overcome this problem has been discussed in this paper. The information update equations of the conventional information filter are modified in terms of fuzzy function of error and change of error, and the results have been found to be encouraging. The efficacy of the technique in preventing divergence is demonstrated in the context of tracking a maneuvering target also. © 2015 Elsevier B.V. All rights reserved.},
author_keywords={Divergence;  Fuzzy function;  Information filter;  Information state;  Multi sensor data fusion},
document_type={Article},
source={Scopus},
}

@ARTICLE{Xing2016359,
author={Xing, Z. and Xia, Y.},
title={Comparison of centralised scaled unscented Kalman filter and extended Kalman filter for multisensor data fusion architectures},
journal={IET Signal Processing},
year={2016},
volume={10},
number={4},
pages={359-365},
doi={10.1049/iet-spr.2015.0205},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84973277794&doi=10.1049%2fiet-spr.2015.0205&partnerID=40&md5=97d7efed906dd13f945630e18d39e946},
affiliation={Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, China},
abstract={This study presents three non-linear centralised scaled unscented Kalman filter (SUKF) for multisensor data fusion algorithms, which are augmented measurements, measurements weighted and sequential filtering fusion. First, the accuracy analysis of extended Kalman filter (EKF) and SUKF is investigated in detail. Second, through comparing the error covariance traces and the absolute mean estimation errors of X and Y directions of centralised SUKF for multisensor data fusion algorithms with that of centralised EKF for multisensor data fusion algorithms, it can be remarked that the performance of centralised augmented measurements SUKF for multisensor data fusion algorithm is the best one among the six algorithms, which is to say that Algorithm (Iu) shows the best performance in accuracy. Finally, combining and synthetically analysing the running time of six algorithms, it illustrates that Algorithm (Iu) is optimal in comprehensive aspects among six algorithms. © The Institution of Engineering and Technology 2016.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Yang20161133,
author={Yang, C.-S.a  b  and Yang, Z.-B.a  and Deng, Z.-L.a },
title={Guaranteed cost robust centralized fusion Kalman predictor with uncertain noise variances},
journal={Kongzhi yu Juece/Control and Decision},
year={2016},
volume={31},
number={6},
pages={1133-1137},
doi={10.13195/j.kzyjc.2015.0451},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84974803240&doi=10.13195%2fj.kzyjc.2015.0451&partnerID=40&md5=11cf50b05b0ecbafe3eaa6b188a992d4},
affiliation={College of Electronic Engineering, Heilongjiang University, Harbin, China; Department of Computer Science and Technology, Heilongjiang College of Business and Technology, Harbin, China},
abstract={A guaranteed cost robust centralized fusion steady-state Kalman predictor is presented for the multisensor system with uncertain noise variances based on the minimax robust estimation principle. A maximal perturbation region of uncertain noise covariances is obtained by using the Lagrange multiplier method. For all admissible perturbations in this region, the deviations of its actual accuracies with respect to the robust accuracy are guaranteed to remain within the prescribed range, and the maximal lower bound and minimal upper bound of accuracy deviations are given. The proof of the guaranteed cost is presented by using the Lyapunov equation approach. A simulation example is given to illustrate the correctness and effectiveness of the proposed method. © 2016, Editorial Office of Control and Decision. All right reserved.},
author_keywords={Guaranteed cost;  Minimax robust estimation;  Multisensor data fusion;  Robust Kalman predictor;  Uncertain noise variances},
document_type={Article},
source={Scopus},
}

@ARTICLE{Brell20163460,
author={Brell, M.a  and Rogass, C.a  and Segl, K.a  and Bookhagen, B.b  and Guanter, L.a },
title={Improving Sensor Fusion: A Parametric Method for the Geometric Coalignment of Airborne Hyperspectral and Lidar Data},
journal={IEEE Transactions on Geoscience and Remote Sensing},
year={2016},
volume={54},
number={6},
pages={3460-3474},
doi={10.1109/TGRS.2016.2518930},
art_number={7399718},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84957623982&doi=10.1109%2fTGRS.2016.2518930&partnerID=40&md5=ab5df678ec0537cd154ca09ed96394f6},
affiliation={Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, Germany; Institute of Earth and Environmental Science, University of Potsdam, Potsdam, Germany},
abstract={Synergistic applications based on integrated hyperspectral and lidar data are receiving a growing interest from the remote-sensing community. A prerequisite for the optimum sensor fusion of hyperspectral and lidar data is an accurate geometric coalignment. The simple unadjusted integration of lidar elevation and hyperspectral reflectance causes a substantial loss of information and does not exploit the full potential of both sensors. This paper presents a novel approach for the geometric coalignment of hyperspectral and lidar airborne data, based on their respective adopted return intensity information. The complete approach incorporates ray tracing and subpixel procedures in order to overcome grid inherent discretization. It aims at the correction of extrinsic and intrinsic (camera resectioning) parameters of the hyperspectral sensor. In additional to a tie-point-based coregistration, we introduce a ray-tracing-based back projection of the lidar intensities for area-based cost aggregation. The approach consists of three processing steps. First is a coarse automatic tie-point-based boresight alignment. The second step coregisters the hyperspectral data to the lidar intensities. Third is a parametric coalignment refinement with an area-based cost aggregation. This hybrid approach of combining tie-point features and area-based cost aggregation methods for the parametric coregistration of hyperspectral intensity values to their corresponding lidar intensities results in a root-mean-square error of 1/3 pixel. It indicates that a highly integrated and stringent combination of different coalignment methods leads to an improvement of the multisensor coregistration. © 2015 IEEE.},
author_keywords={Airborne laser scanning (ALS);  coregistration;  direct georeferencing;  imaging spectroscopy;  multisensor;  parametric georeferencing;  preprocessing;  ray tracing;  rigorous geocoding;  sensor alignment;  sensor fusion},
document_type={Article},
source={Scopus},
}

@ARTICLE{Li20164440,
author={Li, X.a  and Chan, C.-Y.b  and Wang, Y.a },
title={A Reliable Fusion Methodology for Simultaneous Estimation of Vehicle Sideslip and Yaw Angles},
journal={IEEE Transactions on Vehicular Technology},
year={2016},
volume={65},
number={6},
pages={4440-4458},
doi={10.1109/TVT.2015.2496969},
art_number={7317582},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84976506849&doi=10.1109%2fTVT.2015.2496969&partnerID=40&md5=26b554b7739bf8ebf81da8c95f3110ff},
affiliation={School of Instrument Science and Engineering, Southeast University, Nanjing, China; Institute of Transportation Studies, University of California at Berkeley, Berkeley, CA, United States},
abstract={Vehicle sideslip and yaw angles are critical for many vehicle safety systems. Although much research has been presented to obtain them individually, simultaneous accurate estimation of them, based on affordable sensors for land vehicle applications, is seldom reported. This paper proposes a fusion methodology for integrating a single-frequency double-antenna Global Positioning System (DA-GPS) with other low-cost in-vehicle sensors to achieve reliable estimation of both vehicle sideslip and yaw angles. The proposed methodology adopts a hybrid decentralized filtering architecture. First, a vehicle state estimator (VSE) is developed as a virtual sensor to estimate vehicle state information, mainly according to vehicle dynamics. It is composed of two parallel extended Kalman filters (EKFs). Through the interaction of two EKFs, the VSE can adapt to the variations in tire-road friction and accurately estimate the vehicle roll angle. Then, according to vehicle kinematics, a global federated estimator (GFE) is designed, based on the federated filtering algorithm, to achieve the global fusion of inertial sensors, DA-GPS, and VSE. In particular, an adaptive inference mechanism is proposed and introduced into the GFE to adapt to complex driving situations, such as GPS failure, various driving maneuvers, etc. Finally, the proposed method is evaluated via intensive simulations and experiments. The overall results show that the proposed methodology can provide reliable estimation of two angles under a wide range of driving situations. © 2015 IEEE.},
author_keywords={Global positioning system (GPS);  multisensor fusion;  reliable estimation;  sideslip angle;  yaw angle},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Andò2016166,
author={Andò, B.a  and Baglio, S.a  and Lombardo, C.a  and Marletta, V.a  and Dibilio, V.b  and Mostile, G.b  and Nicoletti, A.b  and Zappia, M.b },
title={A multisensor approach to perform instrumented postural instability tests for patient with Parkinson disease},
journal={SAS 2016 - Sensors Applications Symposium, Proceedings},
year={2016},
pages={166-170},
doi={10.1109/SAS.2016.7479839},
art_number={7479839},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988432199&doi=10.1109%2fSAS.2016.7479839&partnerID=40&md5=3e52106d2534f4253b679ebf518fac32},
affiliation={Department of Electric Electronic and Information Engineering, DIEEI, University of Catania, Catania, Italy; Clinica Neurologica AOU Policlinico Vittorio Emanuele of Catania, Catania, Italy},
abstract={Postural instability is one of the symptoms of the Parkinson's disease among rigidity, tremor and bradykinesia, which can cause falls. In order to assess the postural instability of patient with the Parkinson disease the Time Up and Go and the Pull Tests are performed in structured laboratory equipped with expensive monitoring systems. Moreover, the validity of the tests can be compromised by the nonstandard solicitation provided to the patient. The solution proposed in this paper consists of a low cost multisensor system which allows to implement an objective estimation of the input stimulus as well as the assessment of the user response. Results obtained demonstrated the feasibility and the suitability of the methodology proposed. © 2016 IEEE.},
author_keywords={inertial sensors;  multisensor system;  Parkinson's disease;  Postural instability;  Pull Test},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Yunhui2016238,
author={Yunhui, Z. and Xueyun, H.},
title={Application of Multi-sensor Data Fusion in Hot Spot Detection for Air Preheater},
journal={Proceedings - 2015 8th International Symposium on Computational Intelligence and Design, ISCID 2015},
year={2016},
volume={2},
pages={238-242},
doi={10.1109/ISCID.2015.80},
art_number={7469123},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978049970&doi=10.1109%2fISCID.2015.80&partnerID=40&md5=199a1df5be6b583272c9eddbd3607124},
affiliation={School of Mechanical and Electrical Engineering, Shenzhen, China},
abstract={In power station, in order to prevent the air preheater on fire or control the fire damage in time, it is necessary to early detect air preheater ignition precursor, that is, the phenomenon of abnormal internal heat, and timely warning, prevent the fire from the fire. It is also the main target of the development of air preheater hot spot detection system. To improve environmental adaptability and anti-interference ability of the hot spot detection system, and reduce the false alarm rate, in this paper, multi sensor data fusion technology is used in the hot spot detection, that is to say, multi sensor data is analyzed and optimized, integrated to complete the decision. As one of the main methods of information fusion, D-S evidence theory is able to integrate the information provided by multiple sensors, so as to reduce the uncertainty of information. In D-S evidence theory, the synthetic method of conflict evidence is the key to solve the problem of evidence conflict. Therefore, this paper adopts the method of conflict evidence synthesis based on the similarity coefficient of evidence, so as to improve probability of the fire alarm. © 2015 IEEE.},
author_keywords={Air preheate;  conflict evidence;  D-S evidence theory;  hot spot detection;  multi sensor data fusion;  similarity coefficient},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Moody201685,
author={Moody, D.I.a  and Bauer, D.E.b  and Brumby, S.P.a  and Chisolm, E.D.a  and Warren, M.S.a  and Skillman, S.W.a  and Keisler, R.a },
title={Land cover classification in fused multisensor multispectral satellite imagery},
journal={Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation},
year={2016},
volume={2016-April},
pages={85-88},
doi={10.1109/SSIAI.2016.7459181},
art_number={7459181},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971260478&doi=10.1109%2fSSIAI.2016.7459181&partnerID=40&md5=6a3ff2a87c50f3417b063121bb52cc7a},
affiliation={Descartes Labs, Los Alamos, NM, United States; Planet Labs, San Francisco, CA, United States},
abstract={The increase in number of deployed satellite constellations and the improvement in sensing capabilities have led to large volumes of data with a wide range of temporal and spatial coverage. The data analysis capability, however, has been lagging, and has historically focused on single-sensor individual images. We present results from an ongoing effort to develop satellite imagery analysis tools that aggregate information across multiple sensors and bands, and at multiple scales. We focus on field and landmark separation around Clinton, Iowa, and show land cover classification results that combine fused imagery from Planet Labs and Landsat 8. Classification performance is assessed using Cropland Data Layer images generated by USDA. Our method combines spectral, spatial, and temporal information to improve the accuracy of practical land cover classification. © 2016 IEEE.},
author_keywords={data fusion;  multi-INT;  multiscale multispectral satellite imagery;  unsupervised classification},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Rehder2016383,
author={Rehder, J. and Siegwart, R. and Furgale, P.},
title={A General Approach to Spatiotemporal Calibration in Multisensor Systems},
journal={IEEE Transactions on Robotics},
year={2016},
volume={32},
number={2},
pages={383-398},
doi={10.1109/TRO.2016.2529645},
art_number={7445934},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964329498&doi=10.1109%2fTRO.2016.2529645&partnerID=40&md5=2e2b82d54f41f893bf5233f62a630da6},
affiliation={Autonomous Systems Laboratory, ETH Zurich, Zurich, Switzerland},
abstract={With growing demands for accuracy in sensor fusion, increasing attention is being paid to temporal offsets as a source of deterministic error when processing data from multiple devices. Established approaches for the calibration of temporal offsets exploit domain-specific heuristics of common sensor suites and utilize simplifications to circumvent some of the challenges arising when both temporal and spatial parameters are not accurately known a priori. These properties make it difficult to generalize the work to other applications or different combinations of sensors. This work presents a general and principled approach to joint estimation of temporal offsets and spatial transformations between sensors. Our framework exploits recent advances in continuous-time batch estimation and thus exists within the rigorous theoretical framework of maximum likelihood estimation. The derivation is presented without relying on unique properties of specific sensors and, therefore, represents the first general technique for temporal calibration in robotics. The broad applicability of this approach is demonstrated through spatiotemporal calibration of a camera with respect to an inertial measurement unit as well as between a stereo camera and a laser range finder. The method is shown to be more repeatable and accurate than the current state of the art, estimating spatial displacements to millimeter precision and temporal offsets to a fraction of the fastest measurement interval. © 2004-2012 IEEE.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Gokaraju2016,
author={Gokaraju, B.a  and Turlapaty, A.C.c  and Doss, D.A.a  and King, R.L.b  and Younan, N.H.b },
title={Change detection analysis of tornado disaster using conditional copulas and Data Fusion for cost-effective disaster management},
journal={2015 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2015},
year={2016},
doi={10.1109/AIPR.2015.7444537},
art_number={7444537},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84966552702&doi=10.1109%2fAIPR.2015.7444537&partnerID=40&md5=df148397bbaf59dec87a543a00149a9e},
affiliation={University of West Alabama, United States; Mississippi State University, United States; VR Siddhartha Engineering College, India},
abstract={The up-to-date results are presented from an ongoing study of the Data Fusion of multi-temporal and multi-sensor satellite datasets for near real time damage and debris assessment after a tornado disaster event. The space-borne sensor datasets comprising of: (i) C-band SAR dataset from RADARSAT-2; (ii) Multi-Spectral (MS) optical dataset including NIR from RapidEye; (iii) MS and panchromatic dataset of Advanced Linear Imaging (ALI), are studied for multi-sensor data fusion. A combined approach of multi-polarized radiometric and textural feature extraction, and statistical learning based feature classification is devised for fine tuning of the complex and generalized change detection model. We also investigated the use of multi-variate conditional copula as a classifier technique, by formulating the change and no-change as a binary-class classification problem in this study. The classification results from the above technique are used for assessment of damage and debris cover after the tornado disaster event. The performance of the above approach yields a very significant Kappa accuracy up to 75%. A 10-fold cross validation strategy is used for quantitative analysis of the performance of the classification model. This study will be further extended for modelling the effect of incidence angle discrepancies or climatic condition variances, which will address the heterogeneity factor in terms of local statistics of the dataset. © 2015 IEEE.},
author_keywords={change detection;  conditional Copula;  data fusion;  disaster management;  machine learning},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Palumbo201687,
author={Palumbo, F.a  b  and Gallicchio, C.b  and Pucci, R.b  and Micheli, A.b },
title={Human activity recognition using multisensor data fusion based on Reservoir Computing},
journal={Journal of Ambient Intelligence and Smart Environments},
year={2016},
volume={8},
number={2},
pages={87-107},
doi={10.3233/AIS-160372},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962580238&doi=10.3233%2fAIS-160372&partnerID=40&md5=e3e810bc9806d5b396e3fb810c708796},
affiliation={Institute of Information Science and Technologies Alessandro Faedo, National Research Council, Pisa, Italy; Department of Computer Science, University of Pisa, Pisa, Italy},
abstract={Activity recognition plays a key role in providing activity assistance and care for users in smart homes. In this work, we present an activity recognition system that classifies in the near real-time a set of common daily activities exploiting both the data sampled by sensors embedded in a smartphone carried out by the user and the reciprocal Received Signal Strength (RSS) values coming from worn wireless sensor devices and from sensors deployed in the environment. In order to achieve an effective and responsive classification, a decision tree based on multisensor data-stream is applied fusing data coming from embedded sensors on the smartphone and environmental sensors before processing the RSS stream. To this end, we model the RSS stream, obtained from a Wireless Sensor Network (WSN), using Recurrent Neural Networks (RNNs) implemented as efficient Echo State Networks (ESNs), within the Reservoir Computing (RC) paradigm. We targeted the system for the EvAAL scenario, an international competition that aims at establishing benchmarks and evaluation metrics for comparing Ambient Assisted Living (AAL) solutions. In this paper, the performance of the proposed activity recognition system is assessed on a purposely collected real-world dataset, taking also into account a competitive neural network approach for performance comparison. Our results show that, with an appropriate configuration of the information fusion chain, the proposed system reaches a very good accuracy with a low deployment cost. © 2016 - IOS Press and the authors. All rights reserved.},
author_keywords={AAL;  Activity recognition;  Neural networks;  Reservoir Computing;  Sensor data fusion;  WSN},
document_type={Article},
source={Scopus},
}

@ARTICLE{Dolz2016982,
author={Dolz, D. and Peñarrocha, I. and Sanchis, R.},
title={Jump state estimation with multiple sensors with packet dropping and delaying channels},
journal={International Journal of Systems Science},
year={2016},
volume={47},
number={4},
pages={982-993},
doi={10.1080/00207721.2014.907944},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941876697&doi=10.1080%2f00207721.2014.907944&partnerID=40&md5=e87201b50775437c1dbb9ebe8153f412},
affiliation={Departament D'Enginyeria de Sistemes Industrials i Disseny, Universitat Jaume i, Casetelló, Spain},
abstract={This work addresses the design of a state observer for systems whose outputs are measured through a communication network. The measurements from each sensor node are assumed to arrive randomly, scarcely and with a time-varying delay. The proposed model of the plant and the network measurement scenarios cover the cases of multiple sensors, out-of-sequence measurements, buffered measurements on a single packet and multirate sensor measurements. A jump observer is proposed that selects a different gain depending on the number of periods elapsed between successfully received measurements and on the available data. A finite set of gains is pre-calculated offline with a tractable optimisation problem, where the complexity of the observer implementation is a design parameter. The computational cost of the observer implementation is much lower than in the Kalman filter, whilst the performance is similar. Several examples illustrate the observer design for different measurement scenarios and observer complexity and show the achievable performance. © 2014 Taylor & Francis.},
author_keywords={bufferised measurement;  multisensor systems;  networked control systems;  out-of-sequence measurements (OOSMs);  packet dropouts;  packet-delaying networks;  state estimation;  stochastic stability;  wireless sensor network},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Scherbaum2016,
author={Scherbaum, P. and Brauchle, J. and Kraft, T. and Pless, S.},
title={MACS-Mar - A real-time capable multisensor remote sensing system for maritime applications: Current state of development},
journal={Proceedings of the 2015 IEEE International Conference on Aerospace Electronics and Remote Sensing, ICARES 2015},
year={2016},
doi={10.1109/ICARES.2015.7429839},
art_number={7429839},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84966472161&doi=10.1109%2fICARES.2015.7429839&partnerID=40&md5=42196d258b68022c5cc6dad148e1c52a},
affiliation={German Aerospace Center (DLR), Institute of Optical Sensor Systems, Berlin, Germany},
abstract={This article describes a new version of the Modular Airborne Camera System (MACS) [1], which has been made especially for maritime applications. The system is a successor to a High Altitude Long Endurance (HALE) MACS system which was operated in the Himalayas in 2014 [2]. Scenarios in the maritime environment require a different constellation of sensor heads, as shown in this paper. Furthermore, a method of supplying real-time capabilities to ensure flexibility in operation, as well as photogrammetric evaluation, is described. An overview of current and planned results will give an idea of the camera system's potential. The scene to be monitored is an unchanging surface; in general, nothing but water can be seen. Every change of this condition needs to be detected and classified automatically and in real-time. To ensure this, it is necessary to have more than just a camera capturing visible light, so a thermal infrared capturing camera and a hyperspectral sensor are also used. The classification results derived onboard are sent to a ground station by radio downlink. An operator in the command and control facility is then able to use this georeferenced semantic information to decide on the next steps. © 2015 IEEE.},
author_keywords={aerial camera;  data fusion;  hyperspectral;  MACS;  maritime security;  real-time;  remote sensing;  thermal infrared},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Chen2016265,
author={Chen, H.},
title={Research on multi-sensor data fusion technology based on PSO-RBF neural network},
journal={Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015},
year={2016},
pages={265-269},
doi={10.1109/IAEAC.2015.7428560},
art_number={7428560},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84966312473&doi=10.1109%2fIAEAC.2015.7428560&partnerID=40&md5=a92fa80d743fa5995751eb643af6eb48},
affiliation={School of Physics, Tonghua Normal College, Tonghua, China},
abstract={The paper mainly aims at cross sensitivity problems in the measurement process with pressure sensor. Cross sensitivity problems are mainly manifested in the following aspects: pressure measurement is affected by target quantity and interfered by non-target quantity synchronously, such as temperature and other factors. New thought is provided for sensor data fusion due to continuous development of artificial neural network technology. In the paper, multi-sensor data fusion algorithm based on PSO-RBF neural network is proposed on the basis of technology for studying commonly used data fusion methods. PSO Particle Swarm Optimization is utilized for optimizing weight and base width of RBF neural network. Influence of non-target quantity can be eliminated through RBF neural network algorithm under the precondition of fully considering non-target quantities, such as temperature, etc. thereby improving measurement precision of pressure sensor. © 2015 IEEE.},
author_keywords={data fusion;  neural network;  pressure sensor;  PSO;  RBF},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Tuia201646,
author={Tuia, D.a  and Moser, G.b  and Saux, B.L.c },
title={2016 IEEE GRSS Data Fusion Contest: Very high temporal resolution from space Technical Committees},
journal={IEEE Geoscience and Remote Sensing Magazine},
year={2016},
volume={4},
number={1},
pages={46-48},
doi={10.1109/MGRS.2016.2515107},
art_number={7451302},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964659855&doi=10.1109%2fMGRS.2016.2515107&partnerID=40&md5=48a0908e7a27454e46bd0fce0e431eeb},
affiliation={GIUZ, University of Zurich, Zurich, Switzerland; Dept. of Biophysical and Electronic Eng. (DIBE), University of Genoa, Genoa, Italy; Traitement de l'Information et Modélisation, ONERA Palaiseau, Palaiseau, Île-de-France, France},
abstract={The 2016 Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS), aims at providing a challenging image analysis opportunity including multitemporal, multiresolution, and multisensor fusion. The 2016 contest involves two data modalities acquired over the city of Vancouver, Canada (49?15?N 123?6?W): a very high-resolution multitemporal sequence and an ultrahigh definition (UHD) video from space acquired from the International Space Station (ISS). The data were acquired and provided by Deimos Imaging and UrtheCast (Figure 1). © 2013 IEEE.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Pathirana20161822,
author={Pathirana, P.N.a  and Li, S.a  and Trinh, H.M.a  and Seneviratne, A.b },
title={Robust Real-Time Bio-Kinematic Movement Tracking Using Multiple Kinects for Tele-Rehabilitation},
journal={IEEE Transactions on Industrial Electronics},
year={2016},
volume={63},
number={3},
pages={1822-1833},
doi={10.1109/TIE.2015.2497662},
art_number={7317756},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962425393&doi=10.1109%2fTIE.2015.2497662&partnerID=40&md5=0f7a5062e609bb096b7ac02c244f4669},
affiliation={School of Engineering, Deakin University, Waurn Ponds, VIC, Australia; National ICT Australia (NICTA), Sydney, NSW, Australia},
abstract={This paper investigates the robust and accurate capture of human joint poses and bio-kinematic movements for exercise monitoring in real-time tele-rehabilitation applications. Recently developed model-based estimation ideas are used to improve the accuracy, robustness, and real-time characteristics considered vital for applications, where affordability and domestic use are the primary focus. We use the spatial diversity of the arbitrarily positioned Microsoft Kinect receivers to improve the reliability and promote the uptake of the concept. The skeleton-based information is fused to enhance accuracy and robustness, critical for biomedical applications. A specific version of a robust Kalman filter (KF) in a linear framework is employed to ensure superior estimator convergence and real-time use, compared to other commonly used filters. The algorithmic development was conducted in a generic form and computer simulations were conducted to verify our assertions. Hardware implementations were carried out to test the viability of the proposed state estimator in terms of the core requirements of reliability, accuracy, and real-time use. Performance of the overall system implemented in an information fusion context was evaluated against the commercially available and industry standard Vicon system for different exercise routines, producing comparable results with much less infrastructure and financial investment. © 1982-2012 IEEE.},
author_keywords={Automated human movement assessment;  biomedical signal processing;  multisensor system;  robust linear filtering;  tele-rehabilitation},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Dong20161175,
author={Dong, J. and Xu, Q.},
title={Multi-sensor data fusion algorithm for automobile anti-theft system},
journal={Proceedings - 5th International Conference on Instrumentation and Measurement, Computer, Communication, and Control, IMCCC 2015},
year={2016},
pages={1175-1178},
doi={10.1109/IMCCC.2015.252},
art_number={7406031},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963979914&doi=10.1109%2fIMCCC.2015.252&partnerID=40&md5=c929980fcaa0f486790f48a28eda8491},
affiliation={School of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin, China},
abstract={Automobile anti-theft system need to use Multisensor data fusion. For the problems in the process of multisensor data fusion, D-S evidence theory based on the weight distribution is corrected using the theory of matrix analysis. After verification, the algorithm can deal with the integration process operation problem of excessive very well. In the case of failure of one or more sensors this algorithm can ensure the accuracy of the results of the integration. © 2015 IEEE.},
author_keywords={D-S evidence theory;  Matrix analysis;  Multi-sensor data fusion},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Dawood20161,
author={Dawood, M.a  b  and Cappelle, C.c  and El Najjar, M.E.a  and Khalil, M.b  and El Hassan, B.b  and Pomorski, D.a  and Peng, J.a },
title={Virtual 3D city model as a priori information source for vehicle localization system},
journal={Transportation Research Part C: Emerging Technologies},
year={2016},
volume={63},
pages={1-22},
doi={10.1016/j.trc.2015.12.003},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84951781400&doi=10.1016%2fj.trc.2015.12.003&partnerID=40&md5=12a8b19c9be85204983d78405f89ef99},
affiliation={Lille 1 University, LAGIS, UMR CNRS 8164, Lille, France; Lebanese University, Doctoral School for Sciences and Technology, AZM Center for Research in Biotechnology, Tripoli, Lebanon; Université de Technologie de Belfort-Montbéliard, Laboratoire Systèmes et Transports, Belfort, France},
abstract={This paper aims at demonstrating the usefulness of integrating virtual 3D models in vehicle localization systems. Usually, vehicle localization algorithms are based on multi-sensor data fusion. Global Navigation Satellite Systems GNSS, as Global Positioning System GPS, are used to provide measurements of the geographic location. Nevertheless, GNSS solutions suffer from signal attenuation and masking, multipath phenomena and lack of visibility, especially in urban areas. That leads to degradation or even a total loss of the positioning information and then unsatisfactory performances. Dead-reckoning and inertial sensors are then often added to back up GPS in case of inaccurate or unavailable measurements or if high frequency location estimation is required. However, the dead-reckoning localization may drift in the long term due to error accumulation. To back up GPS and compensate the drift of the dead reckoning sensors based localization, two approaches integrating a virtual 3D model are proposed in registered with respect to the scene perceived by an on-board sensor. From the real/virtual scenes matching, the transformation (rotation and translation) between the real sensor and the virtual sensor (whose position and orientation are known) can be computed. These two approaches lead to determine the pose of the real sensor embedded on the vehicle. In the first approach, the considered perception sensor is a camera and in the second approach, it is a laser scanner. The first approach is based on image matching between the virtual image extracted from the 3D city model and the real image acquired by the camera. The two major parts are: 1. Detection and matching of feature points in real and virtual images (three features points are compared: Harris corner detector, SIFT and SURF). 2. Pose computation using POSIT algorithm. The second approach is based on the on-board horizontal laser scanner that provides a set of distances between it and the environment. This set of distances is matched with depth information (virtual laser scan data), provided by the virtual 3D city model. The pose estimation provided by these two approaches can be integrated in data fusion formalism. In this paper the result of the first approach is integrated in IMM UKF data fusion formalism. Experimental results obtained using real data illustrate the feasibility and the performances of the proposed approaches. © 2015 Elsevier Ltd.},
author_keywords={3D-GIS;  Data fusion;  GPS;  Image processing;  Intelligent vehicle;  Localization},
document_type={Article},
source={Scopus},
}

@ARTICLE{Huang201652,
author={Huang, J.a  and Yan, B.a  and Chen, H.b  and Guo, H.c },
title={Track-to-track fusion with global feedback based on split covariance intersection},
journal={Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition)},
year={2016},
volume={44},
number={1},
pages={52-55},
doi={10.13245/j.hust.160111},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961795093&doi=10.13245%2fj.hust.160111&partnerID=40&md5=ff2a458428e6460cf15f73dc042fdd29},
affiliation={Department of Weaponry Engineering, Naval University of Engineering, Wuhan, China; College of Electronics Engineering, National University of Defense Technology, Changsha, China; 63981 Unit, The Peoples Liberation Arm, Wuhan, China},
abstract={For multi-sensor data fusion with unknown cross-covariance, a track-to-track fusion with global feedback based on covariance intersection method was proposed. This method separates the whole covariance into two parts, one is the correlate information error covariance and another is independent information error covariance. Based on this, the form of the split Kalman filter was proposed. Further, a distributed fusion structure with global information feedback was designed. This method overcomes the shortcoming of that the whole information was kneaded together in the method of the simple convex combination fusion or the traditional covariance intersection fusion. Last, a simulation example showed that this method has a smaller mean square error than that two methods. © 2016, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.},
author_keywords={Estimation error;  Global feedback;  Kalman filter;  Split covariance intersection;  Track-to-track fusion},
document_type={Article},
source={Scopus},
}

@ARTICLE{Manes2016,
author={Manes, G.a  and Collodi, G.a  and Gelpi, L.b  and Fusco, R.b  and Ricci, G.b  and Manes, A.c  and Passafiume, M.a },
title={Realtime gas emission monitoring at hazardous sites using a distributed point-source sensing infrastructure},
journal={Sensors (Switzerland)},
year={2016},
volume={16},
number={1},
doi={10.3390/s16010121},
art_number={121},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955067420&doi=10.3390%2fs16010121&partnerID=40&md5=1da58f42b335b1fe74b97db88b1f904d},
affiliation={Department of Information Engineering (DINFO), Università di Firenze, via Santa Marta 3, Firenze, Italy; Eni Spa; Piazzale Enrico Mattei, Roma, Italy; Netsens Srl, via S.Pertini 93, Sesto Fiorentino, FI, Italy},
abstract={This paper describes a distributed point-source monitoring platform for gas level and leakage detection in hazardous environments. The platform, based on a wireless sensor network (WSN) architecture, is organised into sub-networks to be positioned in the plant’s critical areas; each sub-net includes a gateway unit wirelessly connected to the WSN nodes, hence providing an easily deployable, stand-alone infrastructure featuring a high degree of scalability and reconfigurability. Furthermore, the system provides automated calibration routines which can be accomplished by non-specialized maintenance operators without system reliability reduction issues. Internet connectivity is provided via TCP/IP over GPRS (Internet standard protocols over mobile networks) gateways at a one-minute sampling rate. Environmental and process data are forwarded to a remote server and made available to authenticated users through a user interface that provides data rendering in various formats and multi-sensor data fusion. The platform is able to provide real-time plant management with an effective; accurate tool for immediate warning in case of critical events. © 2016 by the authors; licensee MDPI, Basel, Switzerland.},
author_keywords={Photoionization detectors;  VOC monitoring;  Wireless sensor networks},
document_type={Article},
source={Scopus},
}

@ARTICLE{Heideklang2016,
author={Heideklang, R.a  and Shokouhi, P.b },
title={Decision-level fusion of spatially scattered multi-modal data for nondestructive inspection of surface defects},
journal={Sensors (Switzerland)},
year={2016},
volume={16},
number={1},
doi={10.3390/s16010105},
art_number={105},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84987677705&doi=10.3390%2fs16010105&partnerID=40&md5=9a7dfc7b4707c84861dac8fe7608b436},
affiliation={Division 8.5 Micro NDE, Federal Institute for Materials Research and Testing, Unter den Eichen 87, Berlin, Germany; Department of Civil and Environmental Engineering, The Pennsylvania State University, 215 Sackett Bldg., University Park, PA, United States},
abstract={This article focuses on the fusion of flaw indications from multi-sensor nondestructive materials testing. Because each testing method makes use of a different physical principle, a multi-method approach has the potential of effectively differentiating actual defect indications from the many false alarms, thus enhancing detection reliability. In this study, we propose a new technique for aggregating scattered two- or three-dimensional sensory data. Using a density-based approach, the proposed method explicitly addresses localization uncertainties such as registration errors. This feature marks one of the major of advantages of this approach over pixel-based image fusion techniques. We provide guidelines on how to set all the key parameters and demonstrate the technique’s robustness. Finally, we apply our fusion approach to experimental data and demonstrate its capability to locate small defects by substantially reducing false alarms under conditions where no single-sensor method is adequate. © 2016 by the authors; licensee MDPI, Basel, Switzerland.},
author_keywords={Defect detection;  Density estimation;  Multi-sensor data fusion;  Nondestructive testing;  Registration errors;  Scattered data},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Muravyov2016293,
author={Muravyov, S.V. and Khudonogova, L.I.},
title={Multisensor accuracy enhancement on the base of interval voting in form of preference aggregation in WSN for ecological monitoring},
journal={International Congress on Ultra Modern Telecommunications and Control Systems and Workshops},
year={2016},
volume={2016-January},
pages={293-297},
doi={10.1109/ICUMT.2015.7382445},
art_number={7382445},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964607490&doi=10.1109%2fICUMT.2015.7382445&partnerID=40&md5=e10ccbf458f172f7b2886085ffcc98d8},
affiliation={Dept. of Computer-Aided Measurement Systems and Metrology, National Research Tomsk Polytechnic University, Tomsk, Russian Federation},
abstract={In wireless sensor networks (WSNs) for ecological monitoring, sensing performance degrades over time due to harsh environmental conditions or battery depletion. Calibration of multisensors requires extra temporal, financial or energy resources. In order to enhance an accuracy of the multisensor measurements, we propose an approach based on interval voting in form of preference aggregation. The approach allows to determine a corrected value of a measured parameter, lying within narrow uncertainty bounds, on the basis of inaccurate measurement data, obtained from neighboring multisensors. Simulation results have shown that the proposed approach allows to achieve considerably better accuracy comparatively to other known interval voting techniques. © 2015 IEEE.},
author_keywords={ecological monitoring;  interval voting;  Kemeny ranking;  multisensor data fusion;  preference aggregation;  wireless sensor network},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Huy20161,
author={Huy, D.V.a  and Viet, N.D.b },
title={DF-AMS: Proposed Solutions for Multi-Sensor Data Fusion in Wireless Sensor Networks},
journal={Proceedings - 2015 IEEE International Conference on Knowledge and Systems Engineering, KSE 2015},
year={2016},
pages={1-6},
doi={10.1109/KSE.2015.28},
art_number={7371749},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964739527&doi=10.1109%2fKSE.2015.28&partnerID=40&md5=8aa3957f27cfde9e3683da529fdcd707},
affiliation={Science, Technology and Environment Department, Ministry of Culture, Sports and Tourism, Hanoi, Viet Nam; University of Engineering and Technology, Vietnam National University, Hanoi, Hanoi, Viet Nam},
abstract={When using multiple sensor nodes in wireless sensor networks (WSNs) for monitoring (measuring) parameters of the target and sending the result to the base station (BS), data redundancy is an inevitable problem. The measured data often contains the same information, and sending redundant data to BS causes the waste of energy of sensor nodes and the risk of congestion. Multi-sensor data fusion in WSNs is a technology of gathering and processing data applied from node to BS. It improves the performance of surveillance systems by allowing the obtained sensed information from multiple sensor nodes aggregated to one unified format data packet to send to BS to make decision. In this paper, we propose a solution namely DF-AMS (Data Fusion - Average Median Sampling) for sampling and fusing data from the sensor nodes in the cluster to optimize the energy conservation of sensor nodes in clusters and cluster head node. © 2015 IEEE.},
author_keywords={data aggregation;  data fusion;  DF-AMS;  median sampling;  wireless sensor networks},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Xu201634,
author={Xu, Y. and Chen, B. and Hu, Z.},
title={Research for multi-sensor data fusion based on Huffman tree clustering algorithm in greenhouses},
journal={International Journal of Embedded Systems},
year={2016},
volume={8},
number={1},
pages={34-38},
doi={10.1504/IJES.2016.073750},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953239666&doi=10.1504%2fIJES.2016.073750&partnerID=40&md5=990c8055ce7c87589b5b09cbbc334633},
affiliation={College of Information, Northwest A and F University, No. 3, Taicheng Road, Yangling, Shanxi, China},
abstract={Information monitoring system in the greenhouse will often deploy multiple redundant collection nodes in the same region in order to gather environmental parameters. It will improve data collection accuracy and enhance system stability through data fusion. This paper proposes a multi-sensor data fusion algorithm for greenhouse environment monitoring. First, triple exponential smoothing is applied to a variety of greenhouse environmental parameters. Then the smoothed data are sent to a router or gateway, removing invalid data, then idea of the Huffman tree is introduced to determine the order of the integration of each sensor, so as to realise multisensor data fusion. In the Institute for Farmland Irrigation Chinese Academy of Agricultural Sciences, the tests indicated that three exponential smoothing fluctuations significantly reduce the data to improve the accuracy of the data fusion, meanwhile, running time of data fusion algorithm was significantly shorter, and better than weighted average method. © 2016 Inderscience Enterprises Ltd.},
author_keywords={Distributed;  Embedded systems;  Embedded systems;  Greenhouse monitor;  Huffman tree;  Multi-sensor data fusion},
document_type={Article},
source={Scopus},
}

@ARTICLE{Li2016169,
author={Li, Z. and Jiang, H. and Wang, X. and Cai, M.},
title={Research on the algorithm of multi sensor data fusion based on simulated annealing algorithm and genetic algorithm},
journal={Revista de la Facultad de Ingenieria},
year={2016},
volume={31},
number={10},
pages={169-177},
doi={10.21311/002.31.10.16},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85012206964&doi=10.21311%2f002.31.10.16&partnerID=40&md5=e826455aa90f88ee7ec58bf27b1199c6},
affiliation={Modern Educational Technology Center, Shenyang Agricultural University, Shenyang, China},
abstract={We can know from the analysis of the data fusion model of wireless sensor network that data redundancy exists in the data transmission process because of the limitation of computing capability and communication capability of sensor nodes deployed in a cross and repeated manner. Therefore, we need to conduct research on data fusion technology to reduce or remove redundant and invalid data to save communication energy consumption and improve efficiency. This paper conducts research on a type of WSN data fusion algorithm of simulated annealing algorithm on the basis of the merits of global search of genetic algorithm and local search of simulated annealing algorithm. This algorithm can quickly locate the optimal sensor node sequence of mobile agent routing and achieve data fusion with good efficiency. Finally, this paper conduct simulation analysis and verification and the results indicate the validity of this algorithm. © 2016, Universidad Central de Venezuela. All rights reserved.},
author_keywords={Data fusion;  Genetic algorithm;  Multi sensor;  Simulated annealing algorithm},
document_type={Article},
source={Scopus},
}

@ARTICLE{Ji20161363,
author={Ji, S.a  and Chen, Z.-Y.a  and Guo, P.a  and Sun, Y.-J.a  and Shen, J.a  and Wang, J.b  and Lai, C.-F.c },
title={Bayesian approach for multi-sensor data fusion based on compressed sensing for wireless structural damage signal},
journal={Journal of Internet Technology},
year={2016},
volume={17},
number={7},
pages={1363-1371},
doi={10.6138/JIT.2016.17.7.20160415a},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010644436&doi=10.6138%2fJIT.2016.17.7.20160415a&partnerID=40&md5=27f838b38ffbbfe2ff132bb59d4811a3},
affiliation={Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, China; School of Information Engineering, Yangzhou University, China; Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan},
abstract={To meet the requirements of data compression and data fusion in structural health monitoring (SHM) in wireless sensor networks (WSNs), this paper proposes a novel method of multi-sensor data fusion based on compressed sensing (CS) for wireless structural damage signal, which can realize data fusion and reconstruct sparse signals. First, the damage signals of aviation aluminum plate are measured onto the linear measurement data through inner products with random Gaussian matrix. Next, data fusion of measurement data is realized by the Bayesian algorithm. Finally, the damage signals can be reconstructed by the CS method. The experiment results show that, compared with the existing methods, the proposed approach can save the network bandwidth and energy according to a good data fusion performance, anti-noise property and a better data compression effect. The proposed approach can also realize the damage identification accurately on the aviation aluminum plate and keep the detection error within 0.82 mm.},
author_keywords={Bayesian estimation;  Compressed sensing (CS);  Data fusion;  Structural health monitoring (SHM);  Wireless sensor networks (WSNs)},
document_type={Article},
source={Scopus},
}

@ARTICLE{Das2016495,
author={Das, S.a  and Barani, S.b  and Wagh, S.c  and Sonavane, S.S.d },
title={Energy aware routing based on multi-sensor data fusion for wireless sensor networks},
journal={Communications in Computer and Information Science},
year={2016},
volume={628 CCIS},
pages={495-502},
doi={10.1007/978-981-10-3433-6_59},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009484116&doi=10.1007%2f978-981-10-3433-6_59&partnerID=40&md5=750f606ae3c76d90ae6a5c62dfb3967f},
affiliation={Department of CSE, Sathyabama University, Chennai, India; Department of Electronics and Control Engineering, Sathyabama University, Chennai, India; Department of Computer Engineering, KJCOEMR, Pune, India; Department of E&TC, Dr. D.Y. Patil School of Engineering, Pune, India},
abstract={In this paper a multi-sensor data fusion approach for wireless sensor system based on Bayesian strategies and Ant Colony Optimization procedures have been recommended. In this methodology, every node is furnished with various sensors (i.e. temperature and humidity sensors). Usage of more than one sensor gives additional information about the environmental conditions. The data fusion based on the competitive type hierarchical processing has been considered for experimentation. In the data fusion, the data gathered by the sensors are set in the sensing fields and afterward the data fusion probabilities are registered. In our suggested approach, the gathered temperature and humidity information are prepared by multi-sensor data fusion strategies, which then helps in diminishing the energy utilization and also communication cost through accumulation of the repetitive information. The multiple information merging process is reliable and accurate in addition to being energy-efficient which our primary goal is. The proposed algorithms were simulated utilizing Matlab. The implementation of the proposed algorithms were conducted with and without multi-sensor data fusion and the outcomes demonstrate that the proposed algorithms could reduce the energy- consumption, rather save additional energy, thus enhance the entire lifetime of the system. © Springer Nature Singapore Pte Ltd. 2016.},
author_keywords={ACO;  Energy aware routing;  Multi-sensor data fusion;  Optimization techniques;  WSN},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Ren2016,
author={Ren, M.J.a  b  and Liu, M.Y.b  and Cheung, C.F.b  and Yin, Y.H.a },
title={Multi-sensor data fusion for measurement of complex freeform surfaces},
journal={Proceedings of SPIE - The International Society for Optical Engineering},
year={2016},
volume={9903},
doi={10.1117/12.2211638},
art_number={990316},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959927644&doi=10.1117%2f12.2211638&partnerID=40&md5=c351b004cef1913eb8a54780615825e2},
affiliation={Robotics Institute, State Key Laboratory of MSV, Shanghai Jiao Tong University, Shanghai, China; Partner State Key Laboratory of Ultra-precision Machining Technology, Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hong Kong},
abstract={Along with the rapid development of the science and technology in fields such as space optics, multi-scale enriched freeform surfaces are widely used to enhance the performance of the optical systems in both functionality and size reduction. Multi-sensor technology is considered as one of the promising methods to measure and characterize these surfaces at multiple scales. This paper presents a multi-sensor data fusion based measurement method to purposely extract the geometric information of the components with different scales which is used to establish a holistic geometry of the surface via data fusion. To address the key problems of multi-sensor data fusion, an intrinsic feature pattern based surface registration method is developed to transform the measured datasets to a common coordinate frame. Gaussian zero-order regression filter is then used to separate each measured data in different scales, and the datasets are fused based on an edge intensity data fusion algorithm within the same wavelength. The fused data at different scales is then merged to form a new surface with holistic multiscale information. Experimental study is presented to verify the effectiveness of the proposed method. © 2016 SPIE.},
author_keywords={data fusion;  freeform surfaces;  multi-sensor;  Precision measurement;  registration},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Sun2016,
author={Sun, N.a  b  and Zhang, X.a  and Wang, Y.a  b },
title={Multi-sensor data fusion and estimation with poor information based on bootstrap-fuzzy model},
journal={Proceedings of SPIE - The International Society for Optical Engineering},
year={2016},
volume={10025},
doi={10.1117/12.2247790},
art_number={100250J},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011573664&doi=10.1117%2f12.2247790&partnerID=40&md5=8f63d92df8c86572b9814af17d3febee},
affiliation={Beijing Key Laboratory for Optoelectric Measurement Technology, Beijing Information Science and Technology University, Beijing, China; Academy of Opto-electronics, Chinese Academy of Sciences, Beijing, China},
abstract={Multi-sensor data fusion and estimation with poor information is a common problem in the field of stress measurement. Small and distribution unknown data sample obtained from multi-sensor makes the data fusion and estimation much difficult. To solve this problem, a novel bootstrap-fuzzy model is developed. This model is different from the statistical methods and only needs a little data. At first, the limited stress multi-sensor measurement data is expanded by the bootstrap sampling. Secondly, the data fusion sequence is constructed by the bootstrap distribution. Finally the true value and the interval of the stress multi-sensor measurement data are estimated by the fuzzy subordinate functions. Experimental results show that the data fusion sequence is in a good agreement with the original measurement data. The accuracy of the estimated interval can reach 85%. Therefore, the effect of the proposed bootstrap-fuzzy model is validated. © 2016 SPIE.},
author_keywords={Bootstrap-fuzzy model;  Data fusion;  Poor information;  Stress multi-sensor},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Wang20161397,
author={Wang, Q.a  and Li, J.a  and Shen, H.a  and Song, T.a  and Ma, Y.b },
title={Research of multi-sensor data fusion based on binocular vision sensor and laser range sensor},
journal={Key Engineering Materials},
year={2016},
volume={693},
pages={1397-1404},
doi={10.4028/www.scientific.net/KEM.693.1397},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84969820598&doi=10.4028%2fwww.scientific.net%2fKEM.693.1397&partnerID=40&md5=b9d86c7c6fa6cfce908884b7476a2678},
affiliation={School of Mechanical Electronic and Control Engineering, Beijing Jiaotong University, China; Beijing Changcheng Aeronautical Measurement and Control Technology Research Institute, Beijing, China},
abstract={The system of binocular vision sensor was used in the air-to-air close air target positioning in the paper. Due to the limitation of model itself, the measurement accuracy along the direction of optical axis is far lower than the accuracy of vertical direction. In order to improve the measurement accuracy of the optical axis, the paper put forward to using laser range sensor to cooperate with binocular vision sensor; Then the paper proposed adopts adaptive weighted fusion algorithm of multi-sensor information fusion to improve the utilization efficiency of multi-sensor information and to make the results accurately; Finally, the parameters of the system were calibration respectively and experiment is simulated, experimental results show that the position system is feasibility and effectiveness. © 2016 Trans Tech Publications, Switzerland.},
author_keywords={adaptive weighted fusion;  Binocular vision sensor;  Laser range sensor;  Multi-sensor information fusion;  target location},
document_type={Article},
source={Scopus},
}

@ARTICLE{Jin201671,
author={Jin, T.},
title={Multi-sensor data fusion with covariance intersection in robotic space with network sensor devices},
journal={International Journal of Control and Automation},
year={2016},
volume={9},
number={7},
pages={71-78},
doi={10.14257/ijca.2016.9.7.07},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979887671&doi=10.14257%2fijca.2016.9.7.07&partnerID=40&md5=d7e861661d3f313d9ef6c9a5edffc400},
affiliation={Department of Mechatronics Engineering, Dongseo University, South Korea},
abstract={In this paper, as the preliminary step for developing a multi-purpose "Robotic Space" platform to implement advanced technologies easily to realize smart interface to human. We will give an explanation for the Robotic Space system architecture designed and implemented in this study and a short review of existing techniques, since there exists several recent thorough books and review paper on this paper. We will focus on the main results with relevance to the Multi-Sensor Data Fusion with CI of Robotic Space. It is dealt with the general principle of the using the covariance intersection framework to combine mean and covariance estimates without information about their degree of correlation provides a direct solution to the distributed data fusion problem. © 2016 SERSC.},
author_keywords={Data fusion;  Human interface;  Multiple sensor;  Sensor network},
document_type={Article},
source={Scopus},
}

@ARTICLE{Rawat201627,
author={Rawat, S.a  and Rawat, S.b },
title={Multi-sensor data fusion by a hybrid methodology - A comparative study},
journal={Computers in Industry},
year={2016},
volume={75},
pages={27-34},
doi={10.1016/j.compind.2015.10.012},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949921633&doi=10.1016%2fj.compind.2015.10.012&partnerID=40&md5=dc3eff1dadebd53ecabfd6539a4d953a},
affiliation={School of Electronics and Electrical Engineering (SELECT), VIT University, Vellore, India; School of Biotechnology and Biosciences, Lovely Faculty of Technology and Sciences, Punjab, India},
abstract={Multi-sensor data fusion is considered as an inherent problem in wireless sensor network applications. It is widely assumed as a sturdy non linear system in view of the complexities involved in its operation. An accurate and precise methodical solution is therefore a complicated task to accomplish. It is crucial for the sensory systems that they should not be influenced in terms of accuracy and precision by any means. To address these issues a hybrid model employing rough set (RS) with back-propagation neural network (BPNN) is used to ameliorate the data fusion capability of the system with an illustrative example. Experimental results have demonstrated an escalating improvement in the predictive accuracy of the hybrid model as compared to the traditional BPNN model. © 2015 Elsevier B.V. All rights reserved.},
author_keywords={Diagnostic tool;  Hybrid architecture;  Multi-sensor data fusion;  Neural network;  Rough-sets},
document_type={Article},
source={Scopus},
}

@ARTICLE{Gite201685,
author={Gite, S. and Agrawal, H.},
title={On context awareness for multisensor data fusion in IoT},
journal={Advances in Intelligent Systems and Computing},
year={2016},
volume={381},
pages={85-93},
doi={10.1007/978-81-322-2526-3_10},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983134942&doi=10.1007%2f978-81-322-2526-3_10&partnerID=40&md5=af6322b1a1a4878f823e1f4f368473ba},
affiliation={CS/IT Department, SIT, Symbiosis International University, Pune, India},
abstract={With the advances in sensor technology, data mining techniques and the internet, information and communication technology further motivates the development of smart systems such as intelligent transportation systems, smart utilities and smart grid. With the availability of low cost sensors, there is a growing focus on multi-sensor data fusion (MSDF). Internet of Things (IoT) is currently connecting more than 9 billion devices. IoT includes the connectivity of smart things which focuses more on the interactions and interoperations between things and people. Key problem in IoT middleware is to develop efficient decision level intelligent mechanisms. Therefore, we focus on IoT middleware using context-aware mechanism. To get automated inferences of the surrounding environment, context -aware concept is adopted by computing world in combination with data fusion. We conduct a comprehensive review on context awareness for MSDF in IoT and discuss the future directions in the area of context-aware computing. © Springer India 2016.},
author_keywords={Context-aware system;  Dempster–Shafer theory;  IoT;  Multisensor data fusion},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Yan20163,
author={Yan, L. and Jiang, L. and Xia, Y. and Fu, M.},
title={State estimation and data fusion for multirate sensor networks},
journal={International Journal of Adaptive Control and Signal Processing},
year={2016},
volume={30},
number={1},
pages={3-15},
doi={10.1002/acs.2569},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954027163&doi=10.1002%2facs.2569&partnerID=40&md5=4cab151aadeb742c9b92b916a90ab50d},
affiliation={Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, China},
abstract={Summary This article is concerned with state estimation and data fusion of a linear dynamic system observed by multirate sensors in the environment of wireless sensor networks. The sampling, estimation, and transmission rates in the wireless sensor networks are different. We derive the optimal linear estimation in the centralized, sequential, and distributed forms. A numerical example is given to show the feasibility and effectiveness of the proposed algorithms. Copyright © 2015 John Wiley & Sons, Ltd.},
author_keywords={centralized fusion;  distributed fusion;  multirate multisensor;  sequential fusion;  state estimation;  wireless sensor networks},
document_type={Article},
source={Scopus},
}

@ARTICLE{DePaola2016377,
author={De Paola, A. and Ferraro, P. and Gaglio, S. and Lo Re, G.},
title={Context-awareness for multi-sensor data fusion in smart environments},
journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year={2016},
volume={10037 LNAI},
pages={377-391},
doi={10.1007/978-3-319-49130-1_28},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006001651&doi=10.1007%2f978-3-319-49130-1_28&partnerID=40&md5=421825d300746253de1e7c7c222b8bb1},
affiliation={DICGIM Department, University of Palermo, Palermo, Italy},
abstract={Multi-sensor data fusion is extensively used to merge data collected by heterogeneous sensors deployed in smart environments. However, data coming from sensors are often noisy and inaccurate, and thus probabilistic techniques, such as Dynamic Bayesian Networks, are often adopted to explicitly model the noise and uncertainty of data. This work proposes to improve the accuracy of probabilistic inference systems by including context information, and proves the suitability of such an approach in the application scenario of user activity recognition in a smart home environment. However, the selection of the most convenient set of context information to be considered is not a trivial task. To this end, we carried out an extensive experimental evaluation which shows that choosing the right combination of context information is fundamental to maximize the inference accuracy. © Springer International Publishing AG 2016.},
author_keywords={Context awareness;  Dynamic Bayesian Networks;  Multi-sensor data fusion},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Zeng2016,
author={Zeng, W.a  b  and Jiang, X.a  and Lou, S.a  and Scott, P.a },
title={A key point method for data registration for multi-sensor fusion},
journal={Proceedings of the 16th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2016},
year={2016},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984656864&partnerID=40&md5=22fdd7db4fb44b75b091dd2c99da2b3e},
affiliation={EPSRC Centre for Innovative Manufacturing in Advanced Metrology, School of Computing and Engineering, University of Huddersfield, United Kingdom; State Key Lab of Digital Manufacture and Equipment, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China},
abstract={It has been recognized that multi-sensor data fusion can provide a more holistic, accurate and reliable information of the measured surface. Data registration, which is used to align data into one coordinate system, is a key step of data fusion. Widely used feature-based methods find correspondence between features, and then a geometrical transformation is determined to map the target data to the reference data. Reliable and accurate feature selection is thus very important for data registration. In this research, a reliable key point method called Scale Invariant Feature Method (SIFM) for data registration is investigated. By using this method, for each data, one can build a set of feature descriptors of the defined key points, which have the scale/shift/rotation invariant properties. Then the correspondence of two data and geometrical transformation can be achieved by finding the matching of two feature descriptors through closeness measurement. Initial tests on freeform and structured surfaces have proven the effectiveness and efficiency of the method.},
author_keywords={Data registration;  Multi sensor data fusion;  Scale invariant key point},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Duro2016505,
author={Duro, J.A.a  and Padget, J.A.a  and Bowen, C.R.b  and Kim, H.A.b  and Nassehi, A.b },
title={Multi-sensor data fusion framework for CNC machining monitoring},
journal={Mechanical Systems and Signal Processing},
year={2016},
volume={66-67},
pages={505-520},
doi={10.1016/j.ymssp.2015.04.019},
note={cited By 11},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943189817&doi=10.1016%2fj.ymssp.2015.04.019&partnerID=40&md5=44dab2f001a9af8dd467e780a98a2be5},
affiliation={Department of Computer Science, University of Bath, Claverton Down, United Kingdom; Department of Mechanical Engineering, University of Bath, Claverton Down, United Kingdom},
abstract={Reliable machining monitoring systems are essential for lowering production time and manufacturing costs. Existing expensive monitoring systems focus on prevention/detection of tool malfunctions and provide information for process optimisation by force measurement. An alternative and cost-effective approach is monitoring acoustic emissions (AEs) from machining operations by acting as a robust proxy. The limitations of AEs include high sensitivity to sensor position and cutting parameters. In this paper, a novel multi-sensor data fusion framework is proposed to enable identification of the best sensor locations for monitoring cutting operations, identifying sensors that provide the best signal, and derivation of signals with an enhanced periodic component. Our experimental results reveal that by utilising the framework, and using only three sensors, signal interpretation improves substantially and the monitoring system reliability is enhanced for a wide range of machining parameters. The framework provides a route to overcoming the major limitations of AE based monitoring. © 2015 Elsevier Ltd. All rights reserved.},
author_keywords={Acoustic emission;  CNC machining;  Multi-sensor data fusion;  Process monitoring},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Rabani2016,
author={Rabani, A.a  and Senin, N.a  b  and Helmli, F.c  and Butler-Smith, P.a  and Leach, R.a },
title={An integrated measurement method for complex micro-scale geometries},
journal={Proceedings of the 16th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2016},
year={2016},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984638439&partnerID=40&md5=943b9a353cb3fa16e937c5881a6a84ea},
affiliation={Advanced Manufacturing Technology Research Group, University of Nottingham, Nottingham, United Kingdom; Department of Engineering, University of Perugia, Perugia, Italy; Alicona Imaging GmbH, Dr. - Auner - Straße 21a, Raaba, Graz, Austria},
abstract={Micro - scale geometries are becoming commonplace in many high - precision manufacturing applications. Micro - drilling processes , for example, are being employed for producing holes in demanding applications involving fluid transfer, atomisers and micro - mechanic s . This paper explores the measurement and characterisation of a high aspect - ratio micro - hole ( nominal diameter approximately 1000 μm , aspect - ratio 1:10 ), produced using abrasive waterjet in Ti6Al4V. X - ray computed tomography, contact micro - coordinate metrology and focus - variation microscopy are used for measuring the hole surfaces, and dedicated computational geometry algorithms are applied to obtain critical hole dimensions , such as radius as a functi on of depth. The comparison of the measurement and characterisation results obtained by means of the different solutions explored hints at new approaches for multisensor data fusion that can help reduce bias in the measurement of high aspect - ratio micro - scale features.},
author_keywords={Coordinate metrology;  High aspect - ratio micro - scale holes;  Multi - sensor data fusion},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Zhou2016,
author={Zhou, D. and Wang, X. and Cheng, S. and Zhang, X.},
title={An Online Multisensor Data Fusion Framework for Radar Emitter Classification},
journal={International Journal of Aerospace Engineering},
year={2016},
volume={2016},
doi={10.1155/2016/5372510},
art_number={5372510},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975322587&doi=10.1155%2f2016%2f5372510&partnerID=40&md5=b474c24155114fb5f5f53bb676a661fb},
affiliation={Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an, Shaanxi, China},
abstract={Radar emitter classification is a special application of data clustering for classifying unknown radar emitters in airborne electronic support system. In this paper, a novel online multisensor data fusion framework is proposed for radar emitter classification under the background of network centric warfare. The framework is composed of local processing and multisensor fusion processing, from which the rough and precise classification results are obtained, respectively. What is more, the proposed algorithm does not need prior knowledge and training process; it can dynamically update the number of the clusters and the cluster centers when new pulses arrive. At last, the experimental results show that the proposed framework is an efficacious way to solve radar emitter classification problem in networked warfare. © 2016 Dongqing Zhou et al.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Ma2016107,
author={Ma, C.a  and Li, W.a  and Cao, J.a  and Gravina, R.b  and Fortino, G.b },
title={Cloud-based wheelchair assist system for mobility impaired individuals},
journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year={2016},
volume={9864 LNCS},
pages={107-118},
doi={10.1007/978-3-319-45940-0_10},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84989352345&doi=10.1007%2f978-3-319-45940-0_10&partnerID=40&md5=30d1ede649e91894fe10496c491325d9},
affiliation={School of Logistics Engineering, Wuhan University of Technology, Wuhan, China; Department of Informatics, Modeling, Electronics and Systems, University of Calabria, Rende, Italy},
abstract={This paper proposes a new cloud-based wheelchair assist system to support user mobility of impaired people. The smart wheelchair system is equipped with a pressure sensor cushion and accelerometer for posture detection, GPS for localization purposes, and accelerometer for wheelchair status monitoring. Moreover, impaired people are equipped with a body area network to better support user mobility and independence. Our non-invasive system collects accelerometer, pressure data, and GPS signals to recognize user’s daily activities, to track his/her location and to monitor wheelchair status. The proposed system was prototyped using the BodyCloud middleware so as to allow caregivers real-time access to the information generated by the smart wheelchair system. Finally, a case study is proposed to show the effectiveness of the assist system in extracting useful information by using multi-sensor data fusion. © Springer International Publishing AG 2016.},
author_keywords={Body activity;  Bodycloud;  Cloud based;  Multi-sensor data fusion;  Wheelchair assist system;  Wheelchair status},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Ramesh2016355,
author={Ramesh, M. and Jana, S. and Bhat, M.S.},
title={Modified cascade Kalman filter for sensor data fusion in micro aerial vehicle},
journal={IFAC-PapersOnLine},
year={2016},
volume={49},
number={1},
pages={355-360},
doi={10.1016/j.ifacol.2016.03.079},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84974575513&doi=10.1016%2fj.ifacol.2016.03.079&partnerID=40&md5=56bcddb65314e416f16ecace01c278a2},
affiliation={MAV Laboratory, Department of Aerospace, Indian Institute of Science, Bengaluru, India},
abstract={Precise attitude estimation is important for navigation, guidance and control of Micro Aerial Vehicles (MAV) as they are mostly equipped with low integrity sensors due to the constraints on MAV payload and available power. The MAV sensors such as accelerometer and magnetometer are prone to noise as they are placed in proximity to the motor due to constraints on centre of gravity (CG). Data from a single sensor are not reliable for all operating points of flight envelope as the motor vibration and magnetic flux due to the motor vary with RPM. Hence accurate attitude estimation of MAV is achieved through multisensor data fusion. In this paper, a modification to the classical cascade Kalman filter is proposed. Modified Cascade Kalman Filter (MCKF) has better estimation performance as it is a single Kalman filter similar to measurement fusion technique and also it restores the flexibility and computational efficiency of state vector fusion method. A numerical example is presented wherein the derived MCKF is implemented for the attitude estimation of MAV in the 6 DOF simulation model developed in MATLAB/SIMULINK and also for a set of calibrated accelerometer and magnetometer sensor data with motor noise acquired from the autopilot mounted on the rate table of the motion simulator. It was found that MCKF exhibits substantially improved performance compared to extended Kalman filter with only accelerometer data with motor noise. © 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},
author_keywords={Cascade;  Data fusion;  Kalman filters;  Micro aerial vehicles;  Multisensor},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Ye2016,
author={Ye, F. and Chen, J. and Li, Y. and Kang, J.},
title={Decision-Making Algorithm for Multisensor Fusion Based on Grey Relation and DS Evidence Theory},
journal={Journal of Sensors},
year={2016},
volume={2016},
doi={10.1155/2016/3954573},
art_number={3954573},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006154577&doi=10.1155%2f2016%2f3954573&partnerID=40&md5=30c782e074d14ddaed696d5d56063d23},
affiliation={College of Information and Communication Engineering, Harbin Engineering University, Harbin, China},
abstract={Decision-making algorithm, as the key technology for uncertain data fusion, is the core to obtain reasonable multisensor information fusion results. DS evidence theory is a typical and widely applicable decision-making method. However, DS evidence theory makes decisions without considering the sensors' difference, which may lead to illogical results. In this paper, we present a novel decision-making algorithm for uncertain fusion based on grey relation and DS evidence theory. The proposed algorithm comprehensively takes consideration of sensor's credibility and evidence's overall discriminability, which can solve the uncertainty problems caused by inconsistence of sensors themselves and complexity of monitoring environment and simultaneously ensure the validity and accuracy of fusion results. The innovative decision-making algorithm firstly obtains the sensor's credibility through the introduction of grey relation theory and then defines two impact factors as sensor's credibility and evidence's overall discriminability according to the focal element analyses and evidence's distance analysis, respectively; after that, it uses the impact factors to modify the evidences and finally gets more reasonable and effective results through DS combination rule. Simulation results and analyses demonstrate that the proposed algorithm can overcome the trouble caused by large evidence conflict and one-vote veto, which indicates that it can improve the ability of target judgment and enhance precision of uncertain data fusion. Thus the novel decision-making method has a certain application value. © 2016 Fang Ye et al.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Guo201631,
author={Guo, L.a  and Wang, G.b  and Yu, X.a },
title={Design for indoor environment monitoring system based on embedded system and multi-sensor data fusion algorithm},
journal={International Journal of Smart Home},
year={2016},
volume={10},
number={1},
pages={31-40},
doi={10.14257/ijsh.2016.10.1.04},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958956273&doi=10.14257%2fijsh.2016.10.1.04&partnerID=40&md5=1bf08a5e50155a7f5f7c4a57995487b3},
affiliation={Department of Electrical and Mechanical Engineering, Dongguan Polytechnic, Dongguan, Guangdong, China; School of Transportation and Environment, Shenzhen Institute of Information Technology, Shenzhen, China},
abstract={This paper aims to construct an environmental monitoring system for newly decorated room. Digital temperature-humidity sensors, formaldehyde sensors, benzene sensors, ARM11 and Linux embedded system were selected. According to the application characteristics of the sensors, hardware device drivers were designed, generated kernel module files were loaded into the Linux kernel, and the user application programs operating the sensors' devices were written. To get accurate measurement and reliable evaluation for environmental condition, a two-level fusion algorithm was designed, which was composed of data-level fusion based on adaptive weighted fusion algorithm and decision-level fusion based on fuzzy set theory and judging principle of composite index. The system was capable of realizing the real-time acquisition and transmission for environmental data. Thus, the indoor environment quality could be accessed conveniently by users via PC terminal on the Internet. © 2016 SERSC.},
author_keywords={ARM;  Data acquisition;  Data fusion;  Indoor environment;  Monitoring},
document_type={Article},
source={Scopus},
}

@ARTICLE{Kokkonen2016294,
author={Kokkonen, T.},
title={Architecture for the cyber security situational awareness system},
journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year={2016},
volume={9870 LNCS},
pages={294-302},
doi={10.1007/978-3-319-46301-8_24},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84989912985&doi=10.1007%2f978-3-319-46301-8_24&partnerID=40&md5=f09658c2eb99976b39693cdacbea3b0f},
affiliation={Institute of Information Technology, JAMK University of Applied Sciences, Jyväskylä, Finland},
abstract={Networked software systems have a remarkable and critical role in the modern society. There are critical software systems in every business area. At the same time, the amount of cyber-attacks against those critical networked software systems has increased in large measures. Because of that, the cyber security situational awareness of the own assets plays an important role in the business continuity. It should be known what is the current status of the cyber security infrastructure and own assets and what it will be in the near future. For achieving such cyber security situational awareness there is need for the Cyber Security Situational Awareness System. This study presents the novel architecture of the Cyber Security Situational Awareness System. The study also presents the use case of threat mitigation process for such Cyber Security Situational Awareness System. © Springer International Publishing AG 2016.},
author_keywords={Cyber security;  Early warning;  Multi sensor data fusion;  Situational awareness;  Situational awareness information sharing},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Alexandrov2016151,
author={Alexandrov, A.},
title={Ad-hoc kalman filter based fusion algorithm for real-time wireless sensor data integration},
journal={Advances in Intelligent Systems and Computing},
year={2016},
volume={400},
pages={151-159},
doi={10.1007/978-3-319-26154-6_12},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983109582&doi=10.1007%2f978-3-319-26154-6_12&partnerID=40&md5=8933dafb21419843f83d76249e2ab0fa},
affiliation={Institute of Information and Communication Technology, Bulgarian Academy of Sciences, Sofia, Bulgaria},
abstract={The paper describes a new developed software algorithm based on Kalman filtering for multi sensor data fusion designed for Intelligent Wireless Sensor Networks (IWSN). The proposed algorithm is implemented in set of ZigBee 6LowPan based intelligent wireless sensor modules, managed by custom design data integration software platform with SOA architecture. The system consist of ad-hock generated intelligent wireless sensor network for meteorological data collection (include sensors for air temperature, humidity, solar radiation and barometric pressure) and remote control center. Based on the proposed algorithm, the intelligent sensor nodes in the IWSN execute a cluster based decentralized sensor fusion operations of the raw sensor data which reduce the traffic load, minimize the power consumption, decrease the data noise and increase the reliability of the data sent to the control center. © Springer International Publishing Switzerland 2016.},
author_keywords={Ad-hock;  Data integration;  Intelligent WSN;  Kalman filter;  Sensor fusion;  Software algorithm},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Herrera2016810,
author={Herrera, E.P. and Sierra, D.},
title={Multisensor data fusion for pedestrians in spatial disorientation},
journal={29th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2016},
year={2016},
volume={2},
pages={810-815},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017316858&partnerID=40&md5=72ef6dc77093716351d4423e19f0ac68},
affiliation={Universidad Industrial de Santander, Colombia},
abstract={Developments in pedestrian positioning systems present a great potential to assist users with special needs, e.g., people with spatial disorientation issues. In a spatial disorientation episode a person hesitates about her/his surroundings or gets lost in otherwise familiar environments. Research in pedestrian dead reckoning has shown outstanding results about the user's position based on human locomotion patterns. In this paper, we leverage the great advances in the personal positioning field to explore the conditions of a user during a spatial disorientation episode. Here, the parameters commonly used in PDR are utilized to identify events in a spatial disorientation episode. In other words, parameters associated with the user's step are used to identify when the user walks with normal patterns or presents unusual walking patterns. Due to the lack of knowledge of events associated with SDE, experiments were conducted with the aim to observe the process to become disorientated; consequently, an empirical approach is presented. Since this is a preliminary study results were not conclusive, nevertheless, meaningful information was obtained during empirical observations in which a participant was completely immersive in a SDE.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{He2016,
author={He, S.a  and Zhang, J.a  and Cheng, Y.b  and Wan, X.b  and Ran, B.a },
title={Freeway Multisensor Data Fusion Approach Integrating Data from Cellphone Probes and Fixed Sensors},
journal={Journal of Sensors},
year={2016},
volume={2016},
doi={10.1155/2016/7269382},
art_number={7269382},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992386083&doi=10.1155%2f2016%2f7269382&partnerID=40&md5=53dbb8ff992dec45a1239d4e77ae6329},
affiliation={Jiangsu Key Laboratory of Urban ITS, Southeast University, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Collaborative Innovation Center for Technology and Application of Internet of Things, Nanjing, Jiangsu, China; Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI, United States},
abstract={Freeway traffic state information from multiple sources provides sufficient support to the traffic surveillance but also brings challenges. This paper made an investigation into the fusion of a new data combination from cellular handoff probe system and microwave sensors. And a fusion method based on the neural network technique was proposed. To identify the factors influencing the accuracy of fusion results, we analyzed the sensitivity of those factors by changing the inputs of neural-network-based fusion model. The results showed that handoff link length and sample size were identified as the most influential parameters to the precision of fusion. Then, the effectiveness and capability of proposed fusion method under various traffic conditions were evaluated. And a comparative analysis between the proposed method and other fusion approaches was conducted. The results of simulation test and evaluation showed that the fusion method could complement the drawback of each collection method, improve the overall estimation accuracy, adapt to the variable traffic condition (free flow or incident state), suit the fusion of data from cellphone probes and fixed sensors, and outperform other fusion methods. © 2016 Shanglu He et al.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Lorencs201668,
author={Lorencs, A. and Mednieks, I. and Pukitis, M. and Sinica-Sinavskis, J.},
title={Fusion of multisensor data based on different multidimensional distributions},
journal={Elektronika ir Elektrotechnika},
year={2016},
volume={22},
number={4},
pages={68-72},
doi={10.5755/j01.eie.22.4.12600},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983048260&doi=10.5755%2fj01.eie.22.4.12600&partnerID=40&md5=6e4e32ea9b54b8299c069d80d5f7dc57},
affiliation={Institute of Electronics and Computer Science, Dzerbenes iela 14, Riga, Latvia},
abstract={The paper addresses problems related to classification of images obtained by various types of remote sensing devices. Development and use of Bayes type land cover classifiers based on multidimensional Gaussian, Dirichlet and gamma distributions is analysed and compared on the basis of sample data from RGB and hyperspectral thermal sensing devices with unequal spatial resolution. Approaches to data fusion for design of the combined classifiers are presented including the cases where different families of multidimensional distributions are used to model the sensor data and classifiers are designed using combinations of their probability density functions. The best classification results are obtained when the fusion of data from both images is used together with classification based on all three considered distributions combined together.},
author_keywords={Data fusion;  Image classification;  Multidimensional distribution;  Remote sensing.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Cappello20161522,
author={Cappello, F. and Ramasamy, S. and Sabatini, R.},
title={Aircraft dynamics model augmentation of GNSS based navigation and guidance systems for RPAS},
journal={29th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2016},
year={2016},
volume={3},
pages={1522-1530},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017313870&partnerID=40&md5=9083eb71ac94b61ea454e668836b0674},
affiliation={School of Engineering - Aerospace, Mechanical and Manufacturing, RMIT University, Melbourne, VIC, Australia},
abstract={This paper explores the benefits attainable by the adoption of an Aircraft Dynamics Model (ADM) to compensate for the shortcomings of employing low-cost sensors for high dynamics Remotely Piloted Aircraft System (RPAS) attitude determination tasks. Various low-cost sensors are considered for integration in the Navigation and Guidance System (NGS) including Global Navigation Satellite System (GNSS), vision based navigation and Micro- Electromechanical System Inertial Measurement Unit (MEMS-IMU) sensors. The ADM is essentially a knowledge-based module and is used to augment the navigation state vector by predicting the RPAS dynamics (aircraft trajectory and attitude motion). In the presented NGS, the ADM employs a six Degrees-of-Freedom (6- DoF) model. Multi-sensor data fusion is accomplished with a conventional Extended Kalman Filter (EKF) and an advanced Unscented Kalman Filter (UKF). After introducing the key mathematical models describing the 6-DoF ADM, the sensor and integrated system performance are compared in a small RPAS integration scheme (i.e., AEROSONDE RPAS platform) exploring a representative cross-section of the aircraft operational flight envelope and a preliminary sensitivity analysis is performed. In addition to a centralised filter, a dedicated ADM processor (i.e., a local pre-filter) is adopted to to extend the validity time of the virtual sensor across all segments of the RPAS trajectory. Simulation results corroborate the fact that ADM augmentation of GNSS based NGS provides improved performance in terms of attitude data accuracy and a significant extension of the operational validity time is achieved by pre-filtering. © 2016 by Institute of Navigation. All rights reserved.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Makhlouf2016283,
author={Makhlouf, A.a  b  and Marhic, B.a  and Delahoche, L.a  and Clérentin, A.a  and Messaoud, H.b },
title={A smart and predictive heating system using data fusion based on the belief theory},
journal={Studies in Informatics and Control},
year={2016},
volume={25},
number={3},
pages={283-292},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013001617&partnerID=40&md5=c64411102292d945c26fa300666f0f87},
affiliation={Laboratoire des Technologies Innovantes (LTI) EA 3899, Dpt. Informatique, Avenue des Facultés le Bailly, Amiens Cedex 1, France; Laboratoire de Recherche en Automatique, Traitement du Signal et Image (LARATSI), ENIM, Rue Ibn El Jazzar, Monastir, Tunisia},
abstract={This paper investigates the way to model and handle the contextual data uncertainties in order to design a smart heating system that reduces energy consumption. To achieve this, we propose in this paper a data fusion system which provides a trend hypothesis (contextual and near future prediction) with its associated belief. The data to be fused are essentially the occupant's habits, the weather forecast as well as the notion of thermal comfort associated to the occupant's activities. Since the data are uncertain, erroneous and heterogeneous, we propose a multilevel data fusion system based on the belief theory of Dempster-Shafer for data combination and the Transferable Belief Model (TBM) for making the decision which will be a challenge. Despite the data complexity, the simulations are very satisfactory in terms of reducing energy consumption. © 2017, All Rights Reserved.},
author_keywords={Building management system;  Dempster-shafer theory;  Energy saving;  Modelling uncertainty;  Multilevel fusion},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Rubeena2016,
author={Rubeenaa  and Tiwari, K.C.b },
title={Multisensor multiresolution data fusion for improvement in classification},
journal={Proceedings of SPIE - The International Society for Optical Engineering},
year={2016},
volume={9880},
doi={10.1117/12.2222164},
art_number={98800X},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006852774&doi=10.1117%2f12.2222164&partnerID=40&md5=6503cf17a93ef35a879d6b8bbf8aa196},
affiliation={Delhi Technological University, New Delhi, India; Department of Civil Engg, Delhi Technological University, New Delhi, India},
abstract={The rapid advancements in technology have facilitated easy availability of multisensor and multiresolution remote sensing data. Multisensor, multiresolution data contain complementary information and fusion of such data may result in application dependent significant information which may otherwise remain trapped within. The present work aims at improving classification by fusing features of coarse resolution hyperspectral (1 m) LWIR and fine resolution (20 cm) RGB data. The classification map comprises of eight classes. The class names are Road, Trees, Red Roof, Grey Roof, Concrete Roof, Vegetation, bare Soil and Unclassified. The processing methodology for hyperspectral LWIR data comprises of dimensionality reduction, resampling of data by interpolation technique for registering the two images at same spatial resolution, extraction of the spatial features to improve classification accuracy. In the case of fine resolution RGB data, the vegetation index is computed for classifying the vegetation class and the morphological building index is calculated for buildings. In order to extract the textural features, occurrence and co-occurence statistics is considered and the features will be extracted from all the three bands of RGB data. After extracting the features, Support Vector Machine (SVMs) has been used for training and classification. To increase the classification accuracy, post processing steps like removal of any spurious noise such as salt and pepper noise is done which is followed by filtering process by majority voting within the objects for better object classification. © 2016 SPIE.},
author_keywords={Hyperspectral;  Majority Voting;  Morphological Building Index;  Multiresolution;  Multisensor;  Support Vector Machine},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Liandong201653,
author={Liandong, L.},
title={Multi-sensor information fusion method based on BP neural network},
journal={International Journal of Online Engineering},
year={2016},
volume={12},
number={5},
pages={53-57},
doi={10.3991/ijoe.v12i05.5738},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971576963&doi=10.3991%2fijoe.v12i05.5738&partnerID=40&md5=a92aef9dbd36de3b09f660fd805c488d},
affiliation={Electronic Engineering College of HeiLongJiang University, Nangang District of Harbin City University Road 74, HeiLongJiang, Harbin, China},
abstract={This study aims to solve the problem of multisensor information fusion, which is a key issue in the multisensor system development. The main innovation of this study is to propose a novel multi-sensor information fusion algorithm based on back propagation neural network and Bayesian inference. In the proposed algorithm, a triple is defined to represent a probability space; thereafter, the Bayesian inference is used to estimate the posterior expectation. Finally, we construct a simulation environment to test the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm can significantly enhance the accuracy of temperature detection after fusing the data obtained from different sensors.},
author_keywords={Bayesian inference;  BP neural network;  Information fusion;  Probability space},
document_type={Article},
source={Scopus},
}

@ARTICLE{Liandong201653,
author={Liandong, L.},
title={Multi-sensor information fusion method based on BP neural network},
journal={International Journal of Interactive Mobile Technologies},
year={2016},
volume={12},
number={5},
pages={53-57},
doi={10.3991/ijoe.v12i05.5738},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011665455&doi=10.3991%2fijoe.v12i05.5738&partnerID=40&md5=f08f557728af9f1bc5098694e7967446},
affiliation={HeiLongJiang University, Nangang District of Harbin City University Road 74, HeiLongJiang, Harbin, China},
abstract={This study aims to solve the problem of multisensor information fusion, which is a key issue in the multisensor system development. The main innovation of this study is to propose a novel multi-sensor information fusion algorithm based on back propagation neural network and Bayesian inference. In the proposed algorithm, a triple is defined to represent a probability space; thereafter, the Bayesian inference is used to estimate the posterior expectation. Finally, we construct a simulation environment to test the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm can significantly enhance the accuracy of temperature detection after fusing the data obtained from different sensors.},
author_keywords={Bayesian inference;  BP neural network;  Information fusion;  Probability space},
document_type={Article},
source={Scopus},
}

@ARTICLE{Gu2016,
author={Gu, Y.a  and Gross, J.N.a  and Rhudy, M.B.b  and Lassak, K.a },
title={A Fault-Tolerant Multiple Sensor Fusion Approach Applied to UAV Attitude Estimation},
journal={International Journal of Aerospace Engineering},
year={2016},
volume={2016},
doi={10.1155/2016/6217428},
art_number={6217428},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960976281&doi=10.1155%2f2016%2f6217428&partnerID=40&md5=c1965135d0ba5a15cd01d82a6bece23f},
affiliation={Department of Mechanical and Aerospace Engineering (MAE), West Virginia University (WVU), Morgantown, WV, United States; Division of Engineering, Pennsylvania State University, Reading, PA, United States},
abstract={A novel sensor fusion design framework is presented with the objective of improving the overall multisensor measurement system performance and achieving graceful degradation following individual sensor failures. The Unscented Information Filter (UIF) is used to provide a useful tool for combining information from multiple sources. A two-step off-line and on-line calibration procedure refines sensor error models and improves the measurement performance. A Fault Detection and Identification (FDI) scheme crosschecks sensor measurements and simultaneously monitors sensor biases. Low-quality or faulty sensor readings are then rejected from the final sensor fusion process. The attitude estimation problem is used as a case study for the multiple sensor fusion algorithm design, with information provided by a set of low-cost rate gyroscopes, accelerometers, magnetometers, and a single-frequency GPS receiver's position and velocity solution. Flight data collected with an Unmanned Aerial Vehicle (UAV) research test bed verifies the sensor fusion, adaptation, and fault-tolerance capabilities of the designed sensor fusion algorithm. © 2016 Yu Gu et al.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Michel2016,
author={Michel, U. and Mildes, W.},
title={Environmental monitoring in peat bog areas by change detection methods},
journal={Proceedings of SPIE - The International Society for Optical Engineering},
year={2016},
volume={10005},
doi={10.1117/12.2243059},
art_number={100051N},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010778065&doi=10.1117%2f12.2243059&partnerID=40&md5=8ad8fc3d0d1ae44e02e89846a52d80e3},
affiliation={Jade University of Applied Sciences Oldenburg, Germany},
abstract={Remote sensing image analysis systems and geographic information systems (GIS) show great promise for the integration of a wide variety of spatial information supporting tasks such as urban and regional planning, natural resource management, agricultural studies and topographic or thematic mapping. Current and future remote sensing programs are based on a variety of sensors that will provide timely and repetitive multisensor earth observation on a global scale. GIS offer efficient tools for handling, manipulating, analyzing and presenting spatial data that are required for sensible decision making in various areas. The Environmental Monitoring project may serve as a convincing example of the operational use of integrated GIS/remote sensing technologies. The overall goal of the project is to assess the capabilities of satellite remote sensing for the analysis of land use changes, especially in moor areas. These areas are recognized as areas crucial to the mission of the Department of Environment and, therefore, to be placed under an extended level of protection. It is of critical importance, however, to have accurate and current information about the ecological and economic state of these sensitive areas. In selected pasture and moor areas, methods for multisensor data fusion have being developed and tested. The results of this testing show which techniques are useful for pasture and moor monitoring at an operational level. A hierarchical method is used for extracting bog land classes with respect to the environmental protection goals. A highly accurate classification of the following classes was accomplished: deciduous- and mixed forest, coniferous forest, water, very wet areas, meadowland/farmland with vegetation, meadowland/farmland with partly vegetation, meadowland/ farmland without vegetation, peat quarrying with maximum of 50% vegetation, de- and regeneration stages. In addition, a change detection analysis is performed in comparison with the existing classification of 1994-96 [7] and methods are developed to improve the classification strategy. © 2016 SPIE.},
author_keywords={Change detection;  Environmental monitoring;  Peat bog},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Dou20162722,
author={Dou, Y. and Ran, C. and Gao, Y.},
title={Weighted measurement fusion Kalman estimator for multisensor descriptor system},
journal={International Journal of Systems Science},
year={2016},
volume={47},
number={11},
pages={2722-2732},
doi={10.1080/00207721.2015.1018368},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924941818&doi=10.1080%2f00207721.2015.1018368&partnerID=40&md5=20a0db4fb740be58b0004760a538024c},
affiliation={Department of Automation, Heilongjiang University, Harbin, China},
abstract={For the multisensor linear stochastic descriptor system with correlated measurement noises, the fused measurement can be obtained based on the weighted least square (WLS) method, and the reduced-order state components are obtained applying singular value decomposition method. Then, the multisensor descriptor system is transformed to a fused reduced-order non-descriptor system with correlated noise. And the weighted measurement fusion (WMF) Kalman estimator of this reduced-order subsystem is presented. According to the relationship of the presented non-descriptor system and the original descriptor system, the WMF Kalman estimator and its estimation error variance matrix of the original multisensor descriptor system are presented. The presented WMF Kalman estimator has global optimality, and can avoid computing these cross-variances of the local Kalman estimator, compared with the state fusion method. A simulation example about three-sensors stochastic dynamic input and output systems in economy verifies the effectiveness. © 2015 Taylor & Francis.},
author_keywords={Descriptor system;  Global optimality;  Kalman estimator;  Multisensor information fusion;  Weighted measurement fusion},
document_type={Article},
source={Scopus},
}

@ARTICLE{Zhang2016174,
author={Zhang, C.a  and Guo, C.b },
title={Multisensor information fusion scheme based on intelligent particle filter},
journal={Communications in Computer and Information Science},
year={2016},
volume={645},
pages={174-182},
doi={10.1007/978-981-10-2669-0_19},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988841540&doi=10.1007%2f978-981-10-2669-0_19&partnerID=40&md5=74ec8ad2a7c83e10e2cf659418b588b1},
affiliation={Navigation College, Dalian Maritime University, Dalian, China; School of Information Science and Technology, Dalian Maritime University, Dalian, China},
abstract={For the sake of solve the low-quality particles and degeneration in the process of particle filter, Multisensor information fusion based on intelligent particle filter scheme is proposed. The process is divided into two steps, On the one hand, Multi-sensor data is sent to the appropriate particle filter calculation module in order to optimize the particle distribution for the purpose updating the proposed distribution density. On the other hand, By incorporating the algorithm into the likelihood model structured by multi-sensor data, meanwhile, the low-weight particles will be modified into high-weight ones according to genetic operators and the posterior distribution will be more effectively estimated, thus high-quality particles can be obtained. Ultimately, a more precise estimate value will be achieved. A simulation experiment shows the effectiveness of the algorithm. © Springer Science+Business Media Singapore 2016.},
author_keywords={Genetic operators;  Information fusion;  Intelligent particle filter},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Muhammad201614327,
author={Muhammad, M.B and Sarwar, U. and Tahan, M.R. and Abdul Karim, Z.A.},
title={Fault diagnostic model for rotating machinery based on principal component analysis and neural network},
journal={ARPN Journal of Engineering and Applied Sciences},
year={2016},
volume={11},
number={24},
pages={14327-14331},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009230977&partnerID=40&md5=fca03f86730f7990ca001fa0e14380df},
affiliation={Department of Mechanical Engineering, Universiti Teknologi Petronas, Bandar Seri Iskandar, Perak, Malaysia},
abstract={In the current economic challenge, methods to accurately predict system failure has become a holy grail in maintenance with the goal to reduce the cost of unavailability due to unscheduled shutdown. This has led to the current research with the aim to achieve a more accurate fault diagnosis for rotating machinery using a neural network (NN) with principal component analysis (PCA) as a pre-processing step to fuse multiple sensor data. The multisensor data fusion has been proven to improve the fault detection ability for machinery compared to single source condition monitoring. In this paper, an NN-based methodology is presented, where PCA is applied as preprocessing step to detect the rotating machinery faults during operation. The effectiveness of the proposed model is illustrated by a case study on two shaft industrial gas turbine where the real-time performance monitoring data collected from the plant and used to train and test the proposed algorithm. The analysis results show that the PCA-based fusion process has significantly enhanced the performance of NNbased model when compared against NN algorithm without PCA. © 2006-2016 Asian Research Publishing Network (ARPN). All rights reserved.},
author_keywords={Artificial neural network;  Fault diagnosis;  Principal component analysis;  Rotating machinery},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Bish2016,
author={Bish, S.a  and Rohrer, M.a  and Scheffel, P.a  and Bennett, K.b },
title={Multi-sensor fusion development},
journal={Proceedings of SPIE - The International Society for Optical Engineering},
year={2016},
volume={9831},
doi={10.1117/12.2224138},
art_number={98310T},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84987905506&doi=10.1117%2f12.2224138&partnerID=40&md5=db6d44f36502c3f43b9193ede377a7df},
affiliation={McQ Inc., 1551 Forbes St., Fredericksburg, VA, United States; U.S. Army Research Laboratory, 2800 Powder Mill Road, Adelphi, MD, United States},
abstract={The U.S. Army Research Laboratory (ARL) and McQ Inc. are developing a generic sensor fusion architecture that involves several diverse processes working in combination to create a dynamic task-oriented, real-time informational capability. Processes include sensor data collection, persistent and observational data storage, and multimodal and multisensor fusion that includes the flexibility to modify the fusion program rules for each mission. Such a fusion engine lends itself to a diverse set of sensing applications and architectures while using open-source software technologies. In this paper, we describe a fusion engine architecture that combines multimodal and multi-sensor fusion within an Open Standard for Unattended Sensors (OSUS) framework. The modular, plug-and-play architecture of OSUS allows future fusion plugin methodologies to have seamless integration into the fusion architecture at the conceptual and implementation level. Although beyond the scope of this paper, this architecture allows for data and information manipulation and filtering for an array of applications. © 2016 SPIE.},
author_keywords={interoperability;  multimodal fusion;  Open Standards for Unattended Sensors (OSUS);  sensor fusion},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Fonollosa2016444,
author={Fonollosa, J.a  b  and Solórzano, A.a  b  and Jiménez-Soto, J.M.a  b  and Oller-Moreno, S.a  b  and Marco, S.a  b },
title={Smart Chemical System for Reliable Fire Detection},
journal={Procedia Engineering},
year={2016},
volume={168},
pages={444-447},
doi={10.1016/j.proeng.2016.11.540},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009958016&doi=10.1016%2fj.proeng.2016.11.540&partnerID=40&md5=d518e4dc6a898b89eea1b75aa8d5f9f1},
affiliation={Institute for Bioengineering of Catalonia, 10-12 Baldiri Reixac, Barcelona, Spain; University of Barcelona, Department of Engineering: Electronics, 1 Martí i Franquè, Barcelona, Spain},
abstract={In fires, release of volatiles usually happens before generation of smoke/airborne particles. Therefore, fire detection systems based on chemical gas sensors may respond faster than systems based on smoke detectors, which currently cope the market. However, fire detectors based exclusively on gas sensors are prone to false alarms. A key element in those systems is the associated signal and data processing since the detector should discriminate fires form other volatile sources (nuisances). Here, we present a gas sensor array composed of different sensing technologies and a customized data processing algorithm for early and robust fire detection. The gas sensor array was placed in a measurement chamber along with commercially available smoke-based detectors. To test the prototype different types of fire and nuisances were performed in the chamber. Results confirmed the benefits of the gas sensing approach since nuisances were rejected and, for some types of fire, gas-based system triggered the fire alarm faster than the smoke-based detectors.},
author_keywords={Fire alarm;  Fire detector;  Gas sensor array;  Machine Olfaction;  Multisensor system;  Sensor fusion},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Visconti20161220,
author={Visconti, P.a  and Primiceri, P.a  and Costantini, P.a  and Colangelo, G.a  and Cavalera, G.b },
title={Measurement and control system for thermosolar plant and performance comparison between traditional and nanofluid solar thermal collectors},
journal={International Journal on Smart Sensing and Intelligent Systems},
year={2016},
volume={9},
number={3},
pages={1220-1242},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992450119&partnerID=40&md5=592e3cb6fcfd735e2a5605ae329afb67},
affiliation={Department of Innovation Engineering, University of Salento, Lecce, Italy; Company Cavalera S.r.l., Galatone, Lecce, Italy},
abstract={Aim of this work is the design of a programmable electronic system for monitoring the environmental parameters and managing the electrical functions of a thermo-solar plant. The designed control unit detects data from temperature and light sensors, processes acquired information and commands external equipments (pumps, electric valves and power supplies) in order to optimize plant performances and maximize efficiency and energy savings. Recently several researches, in the field of solar thermal energy production, have demonstrated that nanofluid-based solar collectors present higher conversion efficiency. In this context, the designed control unit can be used to detect their operation parameters in order to compare the performances of nanofluidbased solar collector with those of traditional one. The electronic experimental setup is capable to monitor, at the same time, the two different types of solar collector in similar environmental conditions and to show on touch screen display the detected performances. In order to have reference data, experimental measurements have been carried out by using traditional water and Al2O3-based nanofluid thermo solar collectors. The obtained experimental data showed the benefit in terms of efficiency in the use of nanofluid as heat transfer fluid in such a system.},
author_keywords={Control systems;  Electronic equipment;  Energy savings;  Measurement;  Multisensor systems;  Signal processing;  Solar energy;  Solar thermal converters},
document_type={Article},
source={Scopus},
}

@ARTICLE{Tao2016,
author={Tao, J.a  b  and Liu, Y.a  c  and Yang, D.a  d },
title={Bearing Fault Diagnosis Based on Deep Belief Network and Multisensor Information Fusion},
journal={Shock and Vibration},
year={2016},
volume={2016},
doi={10.1155/2016/9306205},
art_number={9306205},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988735586&doi=10.1155%2f2016%2f9306205&partnerID=40&md5=cd994f6f4a42c55e7ddc7359ff1f7603},
affiliation={School of Mechanical and Electrical Engineering, Central South University, Changsha, China; Key Laboratory of Knowledge Processing and Networked Manufacturing, Hunan University of Science and Technology, Xiangtan, China; Light Alloy Research Institute, Central South University, Changsha, China; Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan, China},
abstract={In the rolling bearing fault diagnosis, the vibration signal of single sensor is usually nonstationary and noisy, which contains very little useful information, and impacts the accuracy of fault diagnosis. In order to solve the problem, this paper presents a novel fault diagnosis method using multivibration signals and deep belief network (DBN). By utilizing the DBN's learning ability, the proposed method can adaptively fuse multifeature data and identify various bearing faults. Firstly, multiple vibration signals are acquainted from various fault bearings. Secondly, some time-domain characteristics are extracted from original signals of each individual sensor. Finally, the features data of all sensors are put into the DBN and generate an appropriate classifier to complete fault diagnosis. In order to demonstrate the effectiveness of multivibration signals, experiments are carried out on the individual sensor with the same conditions and procedure. At the same time, the method is compared with SVM, KNN, and BPNN methods. The results show that the DBN-based method is able to not only adaptively fuse multisensor data, but also obtain higher identification accuracy than other methods. © 2016 Jie Tao et al.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wang2016,
author={Wang, L. and Wang, S.},
title={High degree cubature federated filter for multisensor information fusion with correlated noises},
journal={Mathematical Problems in Engineering},
year={2016},
volume={2016},
doi={10.1155/2016/5252487},
art_number={5252487},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84987870746&doi=10.1155%2f2016%2f5252487&partnerID=40&md5=eb38f9d6a17a4640961793bf9a4e9e21},
affiliation={School of Navigation, Guangdong Ocean University, No. 40, Jiefangdong Road, Zhanjiang, China},
abstract={This paper proposes an improved high degree cubature federated filter for the nonlinear fusion system with cross-correlation between process and measurement noises at the same time using the fifth-degree cubature rule and the decorrelated principle in its local filters. The master filter of the federated filter adopts the no-reset mode to fuse local estimates of local filters to generate a global estimate according to the scalar weighted rule. The air-traffic maneuvering target tracking simulations are performed between the proposed filter and the fifth-degree cubature federated filter. Simulations results demonstrate that the proposed filter not only can achieve almost the same accuracy as the fifth-degree cubature federated filter with independent white noises, but also has superior performance to the fifth-degree cubature federated filter while the noises are cross-correlated at the same time. © 2016 Lijun Wang and Sisi Wang.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Li2016,
author={Li, Y.a  and Chen, J.a  and Ye, F.a  and Liu, D.b },
title={The improvement of ds evidence theory and its application in IR/MMW target recognition},
journal={Journal of Sensors},
year={2016},
volume={2016},
doi={10.1155/2016/1903792},
art_number={1903792},
note={

repository: nidiascampos/smartgreen

repo_name: nidiascampos/smartgreen

branch_name: refs/heads/master

gha_language: Python