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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 372161, 25 pages
http://dx.doi.org/10.1155/2012/372161
Research Article

Multiple Maneuvering Target Tracking by Improved Particle Filter Based on Multiscan JPDA

1MOE Key Lab for Intelligent and Networked Systems, Institute of Integrated Automation, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi Province 710049, China
2School of Aeronautics, Northwestern Polytechnical University, Xi’an, Shaanxi Province 710072, China

Received 5 September 2012; Revised 20 October 2012; Accepted 3 November 2012

Academic Editor: Suiyang Khoo

Copyright © 2012 Jing Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The multiple maneuvering target tracking algorithm based on a particle filter is addressed. The equivalent-noise approach is adopted, which uses a simple dynamic model consisting of target state and equivalent noise which accounts for the combined effects of the process noise and maneuvers. The equivalent-noise approach converts the problem of maneuvering target tracking to that of state estimation in the presence of nonstationary process noise with unknown statistics. A novel method for identifying the nonstationary process noise is proposed in the particle filter framework. Furthermore, a particle filter based multiscan Joint Probability Data Association (JPDA) filter is proposed to deal with the data association problem in a multiple maneuvering target tracking. In the proposed multiscan JPDA algorithm, the distributions of interest are the marginal filtering distributions for each of the targets, and these distributions are approximated with particles. The multiscan JPDA algorithm examines the joint association events in a multiscan sliding window and calculates the marginal posterior probability based on the multiscan joint association events. The proposed algorithm is illustrated via an example involving the tracking of two highly maneuvering, at times closely spaced and crossed, targets, based on resolved measurements.