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CLC number: TP391

On-line Access: 2013-06-04

Received: 2012-11-05

Revision Accepted: 2013-03-27

Crosschecked: 2013-05-13

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Journal of Zhejiang University SCIENCE C 2013 Vol.14 No.6 P.417-424

http://doi.org/10.1631/jzus.C1200310


A multiple maneuvering targets tracking algorithm based on a generalized pseudo-Bayesian estimator of first order


Author(s):  Shi-cang Zhang, Jian-xun Li, Liang-bin Wu, Chang-hai Shi

Affiliation(s):  School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; more

Corresponding email(s):   zsc700@sohu.com, lijx@sjtu.edu.cn, lbwu0105@126.com, shi_changhai@sina.com

Key Words:  Gaussian mixture PHD filter, Jump Markov system, Generalized pseudo-Bayesian estimator of first order (GPB1), Multi-target tracking


Shi-cang Zhang, Jian-xun Li, Liang-bin Wu, Chang-hai Shi. A multiple maneuvering targets tracking algorithm based on a generalized pseudo-Bayesian estimator of first order[J]. Journal of Zhejiang University Science C, 2013, 14(6): 417-424.

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author="Shi-cang Zhang, Jian-xun Li, Liang-bin Wu, Chang-hai Shi",
journal="Journal of Zhejiang University Science C",
volume="14",
number="6",
pages="417-424",
year="2013",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1200310"
}

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%T A multiple maneuvering targets tracking algorithm based on a generalized pseudo-Bayesian estimator of first order
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%A Jian-xun Li
%A Liang-bin Wu
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%J Journal of Zhejiang University SCIENCE C
%V 14
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%P 417-424
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%DOI 10.1631/jzus.C1200310

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T1 - A multiple maneuvering targets tracking algorithm based on a generalized pseudo-Bayesian estimator of first order
A1 - Shi-cang Zhang
A1 - Jian-xun Li
A1 - Liang-bin Wu
A1 - Chang-hai Shi
J0 - Journal of Zhejiang University Science C
VL - 14
IS - 6
SP - 417
EP - 424
%@ 1869-1951
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PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1200310


Abstract: 
We describe the design of a multiple maneuvering targets tracking algorithm under the framework of Gaussian mixture probability hypothesis density (PHD) filter. First, a variation of the generalized pseudo-Bayesian estimator of first order (VGPB1) is designed to adapt to the gaussian mixture PHD filter for jump Markov system models (JMS-PHD). The probability of each kinematic model, which is used in the JMS-PHD filter, is updated with VGPB1. The weighted sum of state, associated covariance, and weights for Gaussian components are then calculated. Pruning and merging techniques are also adopted in this algorithm to increase efficiency. Performance of the proposed algorithm is compared with that of the JMS-PHD filter. Monte-Carlo simulation results demonstrate that the optimal subpattern assignment (OSPA) distances of the proposed algorithm are lower than those of the JMS-PHD filter for maneuvering targets tracking.

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Reference

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[4]Li, X.R., Jilkov, V.P., 2003. Survey of maneuvering target tracking. Part I: dynamic models. IEEE Trans. Aerosp. Electron. Syst., 39(4):1333-1364.

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[9]Schuhmacher, D., Vo, B.T., Vo, B.N., 2008. A consistent metric for performance evaluation of multi-object filters. IEEE Trans. Signal Process., 56(8):3447-3457.

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[11]Vo, B.N., Pasha, A., Tuan, H.D., 2006. A Gaussian Mixture PHD Filter for Nonlinear Jump Markov Models. Proc. 45th IEEE Conf. on Decision and Control, p.3162-3167.

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