Bandwidth-efficient target tracking in distributed sensor networks using particle filters

L. Zuo, K. Mehrotra, P. K. Varshney, C. K. Mohan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

29 Scopus citations

Abstract

This paper considers the problem of tracking a moving target in a multisensor environment using distributed particle filters (DPFs). Particle filters have a great potential for solving highly nonlinear and non- Gaussian estimation problems, in which the traditional Kalman Filter(KF) and Extended Kalman Filter(EKF) generally fail. However, in a sensor network, the implementation of distributed particle filters requires huge communications between local sensor nodes and the fusion center. To make the DPF approach feasible for real time processing and to reduce communication requirements, we approximate a posteriori distribution obtained from the local particle filters by a Gaussian Mixture Model(GMM). We propose a modified EM algorithm to estimate the parameters of GMMs obtained locally. These parameters are transmitted to the fusion center where the Best Linear Unbiased Estimator(BLUE) is used for fusion. Simulation results are presented to illustrate the performance of the proposed algorithm.

Original languageEnglish (US)
Title of host publication2006 9th International Conference on Information Fusion, FUSION
DOIs
StatePublished - Dec 1 2006
Event2006 9th International Conference on Information Fusion, FUSION - Florence, Italy
Duration: Jul 10 2006Jul 13 2006

Publication series

Name2006 9th International Conference on Information Fusion, FUSION

Other

Other2006 9th International Conference on Information Fusion, FUSION
CountryItaly
CityFlorence
Period7/10/067/13/06

Keywords

  • Data association
  • EM algorithm
  • Gaussian mixture model
  • Particle filtering
  • Target tracking

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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