Introduction to Algorithms
Distributed particle filters for sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Nonparametric belief propagation for self-calibration in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Energy based acoustic source localization
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Signal Processing
Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
Dynamic Sensor Self-Organization for Distributive Moving Target Tracking
Journal of Signal Processing Systems
Distributed Bayesian fault diagnosis of jump Markov systems in wireless sensor networks
International Journal of Sensor Networks
Distributed Computation of Likelihood Maps for Target Tracking
DCOSS '09 Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems
Collaborative tracking in mobile underwater networks
Proceedings of the Fourth ACM International Workshop on UnderWater Networks
Time-space-sequential distributed particle filtering with low-rate communications
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Robot algorithms for localization of multiple emission sources
ACM Computing Surveys (CSUR)
Robust tracking algorithm for wireless sensor networks based on improved particle filter
Wireless Communications & Mobile Computing
Optimal decentralized Kalman filter and Lainiotis filter
Digital Signal Processing
Multi-robot cooperative spherical-object tracking in 3D space based on particle filters
Robotics and Autonomous Systems
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Two novel distributed particle filters with Gaussian Mixer approximation are proposed to localize and track multiple moving targets in a wireless sensor network. The distributed particle filters run on a set of uncorrelated sensor cliques that are dynamically organized based on moving target trajectories. These two algorithms differ in how the distributive computing is performed. In the first algorithm, partial results are updated at each sensor clique sequentially based on partial results forwarded from a neighboring clique and local observations. In the second algorithm, all individual cliques compute partial estimates based only on local observations in parallel, and forward their estimates to a fusion center to obtain final output. In order to conserve bandwidth and power, the local sufficient statistics (belief) is approximated by a low dimensional Gaussian mixture model(GMM) before propagating among sensor cliques. We further prove that the posterior distribution estimated by distributed particle filter convergence almost surely to the posterior distribution estimated from a centralized bayesian formula. Moreover, a data-adaptive application layer communication protocol is proposed to facilitate sensor self-organization and collaboration. Simulation results show that the proposed DPF with GMM approximation algorithms provide robust localization and tracking performance at much reduced communication overhead.