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distributed Auxiliary particle filtering With Diffusion Strategy for Target Tracking: A Dynamic Event-Triggered Approach

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 328-340页

作者： Song, Weihao Wang, Zidong Wang, Jianan Alsaadi, Fuad E. Shan, Jiayuan Beijing Inst Technol Sch Aerosp Engn Beijing 100081 Peoples R China Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH Middx England King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia

This paper investigates the particle filtering problem for a class of nonlinear/non-Gaussian systems under the dynamic event-triggered protocol. In order to avert frequent data transmission and reduce the communication overhead, a dynamic event-triggered transmission mechanism is adopted to decide whether the data should be transmitted or not. We first consider a scenario where all sensor nodes selectively transmit their newly obtained measurements to a central node, and a full likelihood function at the central node is derived by fusing the transmitted measurements and the information embodied in the non-triggered measurements. Based on the derived full likelihood function, a centralized auxiliary particle filtering algorithm is proposed to select those particles that are more likely to match the current measurement information. Next, based on the diffusion strategy, a distributed auxiliary particle filtering algorithm is further developed, where the local measurements and the local posteriors (approximated by the Gaussian mixture models) are exchanged among neighboring nodes under the dynamic event-triggered communication strategy. Finally, the effectiveness of the proposed filtering schemes is demonstrated via Monte Carlo simulations in a target tracking problem with received-signal-strength sensors.

关键词： Auxiliary particle filtering diffusion strategy distributed particle filtering dynamic event-triggered mechanism nonlinear/non-Gaussian systems

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distributed particle filtering via Optimal Fusion of Gaussian Mixtures

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2018年第2期4卷 280-292页

作者： Li, Jichuan Nehorai, Arye Washington Univ Preston M Green Dept Elect & Syst Engn St Louis MO 63130 USA

We propose a distributed particle filtering algorithm based on an optimal fusion rule for local posteriors. We implement the optimal fusion rule in a distributed and iterative fashion via an average consensus algorithm. We approximate local posteriors as Gaussian mixtures and fuse Gaussian mixtures through importance sampling. We prove that under certain conditions the proposed distributed particle filtering algorithm converges in probability to a global posterior locally available at each sensor in the network. Numerical examples are presented to demonstrate the performance advantages of the proposed method in comparison with other distributed particle filtering algorithms.

关键词： Average consensus data fusion distributed particle filtering Gaussian mixture model importance sampling

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distributed particle filtering Via Optimal Fusion of Gaussian Mixtures 18

Distributed Particle Filtering Via Optimal Fusion of Gaussia...

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18th International Conference on Information Fusion (Fusion)

作者： Li, Jichuan Nehorai, Arye Washington Univ Preston M Green Dept Elect & Syst Engn St Louis MO 63130 USA

ISBN: (纸本)9780982443866

We propose a distributed particle filtering algorithm based on optimal fusion of local posterior estimates. We derive an optimal fusion rule from Bayesian statistics, and implement it in a distributed and iterative fashion via an average consensus algorithm. We approximate local posterior estimates as Gaussian mixtures, and fuse Gaussian mixtures through importance sampling. We prove that under certain conditions the proposed distributed particle filtering algorithm converges to a global posterior estimate locally available at every sensor in the network. Numerical examples are presented to demonstrate the performance advantages of the proposed method in comparison with other posterior-based distributed particle filtering algorithms.

关键词： distributed particle filtering consensus data fusion Gaussian mixture model importance sampling

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distributed DECORRELATION IN SENSOR NETWORKS WITH APPLICATION TO distributed particle filtering

DISTRIBUTED DECORRELATION IN SENSOR NETWORKS WITH APPLICATIO...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Moldaschl, Michael Gansterer, Wilfried N. Hlinka, Ondrej Meyer, Florian Hlawatsch, Franz Univ Vienna Res Grp Theory & Applicat Algorithms Vienna Austria Vienna Univ Technol Inst Telecommun Vienna Austria

ISBN: (纸本)9781479928934

Most distributed statistical signal processing methods assume conditionally uncorrelated sensor measurements although this assumption is often not satisfied. Here, we propose a distributed algorithm for decorrelating the sensor measurements in a wireless sensor network. The algorithm employs a matrix-valued Chebyshev approximation to achieve an approximate decorrelation using only local computations and communication between neighboring sensors. We apply the algorithm to consensus-based distributed particle filtering in a target tracking problem with correlated measurement noises. Simulations show that the decorrelation yields a substantial accuracy improvement while causing only a small communication overhead.

关键词： distributed decorrelation wireless sensor network Chebyshev approximation distributed particle filtering target tracking

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distributed Bernoulli filtering Using Likelihood Consensus

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2019年第2期5卷 218-233页

作者： Papa, Giuseppe Repp, Rene Meyer, Florian Braca, Paolo Hlawatsch, Franz Kognit Corp A-1070 Vienna Austria TU Wien Inst Telecommun A-1040 Vienna Austria Brno Univ Technol Brno 60190 Czech Republic MIT Lab Informat & Decis Syst Cambridge MA 02139 USA NATO STO Ctr Maritime Res & Experimentat I-19126 La Spezia Italy

We consider the detection and tracking of a target in a decentralized sensor network. The presence of the target is uncertain, and the sensor measurements are affected by clutter and missed detections. The state-evolution model and the measurement model may be nonlinear and non-Gaussian. For this practically relevant scenario, we propose a particle-based distributed Bernoulli filter (BF) that provides to each sensor approximations of the Bayes-optimal estimates of the target presence probability and the target state. The proposed method uses all the measurements in the network while requiring only local intersensor communication. This is enabled by an extension of the likelihood consensus method that reaches consensus on the likelihood function under both the target presence and target absence hypotheses. We also propose an adaptive pruning of the likelihood expansion coefficients that yields a significant reduction of intersensor communication. Finally, we present a new variant of the likelihood consensus method that is suited to networks containing star-connected sensor groups. Simulation results show that in challenging scenarios, including a heterogeneous sensor network with significant noise and clutter, the performance of the proposed distributed BF approaches that of the optimal centralized multisensor BE We also demonstrate that the proposed distributed BF outperforms a state-of-the-art distributed BF at the expense of a higher amount of intersensor communication.

关键词： Bernoulli filter distributed target tracking distributed particle filtering likelihood consensus random finite set sensor network

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Approaches to Chernoff fusion with applications to distributed estimation

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DIGITAL SIGNAL PROCESSING 2020年 107卷 102877-102877页

作者： Tslil, Or Lehrer, Nadav Carmi, Avishy Ben Gurion Univ Negev Dept Mech Engn Beer Sheva Israel

In large scale information fusion networks the statistical dependencies between nodes are rarely known. Failing to account for this detail in the design of distributed estimation schemes may lead to statistical inconsistencies and may even cause some estimators to diverge at various nodes within the network. Among the approaches that have been proposed to address this problem, covariance intersection and its generalization are perhaps the most well-known. Owing to its relationship with the statistical divergence known as Chernoff information the latter approach is referred to here as Chernoff fusion. Using Chernoff fusion requires tuneup for the fusion parameters, where in some applications, the Chernoff fusion may cause overhead. This work is concerned with the practical application of Chernoff fusion for distributed estimation. We provide a technique for fast particle filters fusion based on a linear variant of Chernoff fusion as well as a recursive tuning technique of the underlying mixing weights. The viability of the proposed schemes are demonstrated in such applications as distributed object tracking, cooperative robots localization, and image classification with ensembles of convolutional neural networks. (C) 2020 Elsevier Inc. All rights reserved.

关键词： distributed information fusion Chernoff fusion distributed particle filtering Ensembles of neural networks

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Gaussian mixture learning via adaptive hierarchical clustering

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SIGNAL PROCESSING 2018年 150卷 116-121页

作者： Li, Jichuan Nehorai, Arye Washington Univ Dept Elect & Syst Engn St Louis MO 63130 USA

In signal processing, a large number of samples can be generated by a Monte Carlo method and then encoded as a Gaussian mixture model for compactness in computation, storage, and communication. With a large number of samples to learn from, the computational efficiency of Gaussian mixture learning becomes important. In this paper, we propose a new method of Gaussian mixture learning that works both accurately and efficiently for large datasets. The proposed method combines hierarchical clustering with the expectation-maximization algorithm, with hierarchical clustering providing an initial guess for the expectation-maximization algorithm. We also propose adaptive splitting for hierarchical clustering, which enhances the quality of the initial guess and thus improves both the accuracy and efficiency of the combination. We validate the performance of the proposed method in comparison with existing methods through numerical examples of Gaussian mixture learning and its application to distributed particle filtering. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Gaussian mixture learning Adaptive hierarchical clustering Adaptive splitting Expectation-maximization algorithm distributed particle filtering

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Adaptive Resource Allocation in Decentralized Colocated MIMO Radar Network for Multiple Targets Tracking

Adaptive Resource Allocation in Decentralized Colocated MIMO...

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IEEE Radar Conference

作者： Lu, Yanxi He, Zishu Cheng, Ziyang Liu, Shuangli Luo, Xi UESTC Sch Elect Engn Chengdu 611731 Sichuan Peoples R China Natl Key Lab Sience & Technol Space Microwave Xian 710100 Shaanxi Peoples R China

ISBN: (纸本)9781538641675

In this paper, we present a resource-awareness decentralized colocated multiple-input multiple-output radar network for multiple targets tracking. Specifically, we develop a distributed particle filter which incorporates the scheduling of radars. Taking posterior Cramer-Rao lower bound as performance metric, we model the scheduling and power allocation problem of the radar network as a sparse optimization problem. Making use of the sparsity of the solution, the scheduling and power allocation performs jointly. We investigate the decomposable structure of the optimization problem and conduct a decentralized technique to solve it iteratively. We verify the effectiveness of the proposed decentralized radar network with the joint scheduling and power allocation (JSPA) scheme by simulation. The resource utilization is adaptively adjusted according to the states of targets and the performance demand.

关键词： radar network adaptive scheduling and power allocation distributed particle filtering distributed optimization

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A distributed Bernoulli Filter Based on Likelihood Consensus with Adaptive Pruning 21

A Distributed Bernoulli Filter Based on Likelihood Consensus...

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21st International Conference on Information Fusion (FUSION)

作者： Repp, Rene Papa, Giuseppe Meyer, Florian Braca, Paolo Hlawatsch, Franz TU Wien Inst Telecommun A-1040 Vienna Austria Brno Univ Technol Brno 60190 Czech Republic Kognitiv Corp A-1070 Vienna Austria MIT Informat & Decis Syst Lab Cambridge MA 02139 USA NATO STO Ctr Maritime Res & Expt CMRE I-19126 La Spezia Italy

ISBN: (纸本)9780996452762

The Bernoulli filter (BF) is a Bayes-optimal method for target tracking when the target can be present or absent in unknown time intervals and the measurements are affected by clutter and missed detections. We propose a distributed particle-based multisensor BF algorithm that approximates the centralized multisensor BF for arbitrary nonlinear and non-Gaussian system models. Our distributed algorithm uses a new extension of the likelihood consensus (LC) scheme that accounts for both target presence and absence and includes an adaptive pruning of the LC expansion coefficients. Simulation results for a heterogeneous sensor network with significant noise and clutter show that the performance of our algorithm is close to that of the centralized multisensor BF.

关键词： Bernoulli filter distributed target tracking distributed particle filtering likelihood consensus random finite set sensor network

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A distributed Bernoulli Filter Based on Likelihood Consensus with Adaptive Pruning

A Distributed Bernoulli Filter Based on Likelihood Consensus...

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International Conference on Information Fusion

作者： Rene Repp Giuseppe Papa Florian Meyer Paolo Braca Franz Hlawatsch Institute of Telecommunications TU Wien Vienna Austria Brno University of Technology Brno Czech Republic Kognitiv Corporation Vienna Austria Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA

关键词： Bernoulli filter distributed target tracking distributed particle filtering Likelihood consensus Random finite set Sensor network

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