A Bayesian Approach to Track Multiple Extended Targets Using Particle Filter for Nonlinear System

To track multiple extended targets for the nonlinear system, this paper employs the idea of the particle filter to track kinematic states and shape formation of extended targets. First, the Bayesian framework is proposed for multiple extended targets to jointly estimate multiple extended target stat...

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Bibliographic Details
Published inMathematical problems in engineering Vol. 2018; no. 2018; pp. 1 - 10
Main Authors Han, Yulan, Han, Chongzhao
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
Hindawi Limited
Subjects
Algorithms
Bayesian analysis
Computer simulation
Filtration
Hypotheses
International conferences
Mathematical problems
Nonlinear systems
Performance enhancement
Tracking
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Summary:To track multiple extended targets for the nonlinear system, this paper employs the idea of the particle filter to track kinematic states and shape formation of extended targets. First, the Bayesian framework is proposed for multiple extended targets to jointly estimate multiple extended target state and association hypothesis. Furthermore, a joint proposal distribution is defined for the multiple extended target state and association hypothesis. Then, the Bayesian framework of multiple extended target tracking is implemented by the particle filtering which could release the high computational burden caused by the increase in the number of extended targets and measurements. Simulation results show that the proposed multiple extended target particle filter has superior performance in shape estimation and improves the performance of the position estimation in the situation that there are spatially closed extended targets.
ISSN:1024-123X
1563-5147
DOI:10.1155/2018/7424538