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

Mathematical Problems in Engineering. 2012;2012 DOI 10.1155/2012/372161

 

Journal Homepage

Journal Title: Mathematical Problems in Engineering

ISSN: 1024-123X (Print); 1563-5147 (Online)

Publisher: Hindawi Publishing Corporation

LCC Subject Category: Technology: Engineering (General). Civil engineering (General) | Science: Mathematics

Country of publisher: Egypt

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML

 

AUTHORS

Jing Liu (MOE 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)
ChongZhao Han (MOE 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)
Feng Han (MOE 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)
Yu Hu (School of Aeronautics, Northwestern Polytechnical University, Xi_an, Shaanxi Province 710072, China)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 26 weeks

 

Abstract | Full Text

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.