Regular Paper
International Journal of Control, Automation and Systems 2013; 11(5): 947-956
Published online October 9, 2013
https://doi.org/10.1007/s12555-012-0177-z
© The International Journal of Control, Automation, and Systems
A Novel Multiple Maneuvering Targets Tracking Algorithm with Data Association and Track Management
A novel multiple maneuvering targets tracking algorithm with data association and track management is presented in this paper. First, the variation of the generalized pseudo-Bayesian estimator of first order is designed. Then, the data association and track management via handling two matrices are given, which reflect the relationships between target trajectory and the output of the Gaussian mixture probability hypothesis density (PHD) filter for jump Markov system models (JMS-GM-PHD) filter. The tracking performance of the proposed algorithm is compared with two conventional algorithms. One is JMS-GM-PHD filter, the other is algorithm entitled hybrid algorithms for multi-target tracking using MHT and GM-CPHD which is denoted as hybrid method hereinafter. The results of Monte Carlo simu-lation show that the proposed filter has overall performance than the conventional.
Keywords Data association and track management, generalized pseudo-Bayesian of first order, pseudo-measurement.
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Regular Paper
International Journal of Control, Automation and Systems 2013; 11(5): 947-956
Published online October 1, 2013 https://doi.org/10.1007/s12555-012-0177-z
Copyright © The International Journal of Control, Automation, and Systems.
A Novel Multiple Maneuvering Targets Tracking Algorithm with Data Association and Track Management
Shicang Zhang*, Jianxun Li, and Liangbin Wu
Shanghai Jiao Tong University
Abstract
A novel multiple maneuvering targets tracking algorithm with data association and track management is presented in this paper. First, the variation of the generalized pseudo-Bayesian estimator of first order is designed. Then, the data association and track management via handling two matrices are given, which reflect the relationships between target trajectory and the output of the Gaussian mixture probability hypothesis density (PHD) filter for jump Markov system models (JMS-GM-PHD) filter. The tracking performance of the proposed algorithm is compared with two conventional algorithms. One is JMS-GM-PHD filter, the other is algorithm entitled hybrid algorithms for multi-target tracking using MHT and GM-CPHD which is denoted as hybrid method hereinafter. The results of Monte Carlo simu-lation show that the proposed filter has overall performance than the conventional.
Keywords: Data association and track management, generalized pseudo-Bayesian of first order, pseudo-measurement.
Vol. 22, No. 9, pp. 2673~2953
