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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
Cited: 0
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A multiple maneuvering targets tracking algorithm based on a generalized pseudo-Bayesian estimator of first order
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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.
@article{title="A multiple maneuvering targets tracking algorithm based on a generalized pseudo-Bayesian estimator of first order",
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"
}
%0 Journal Article
%T A multiple maneuvering targets tracking algorithm based on a generalized pseudo-Bayesian estimator of first order
%A Shi-cang Zhang
%A Jian-xun Li
%A Liang-bin Wu
%A Chang-hai Shi
%J Journal of Zhejiang University SCIENCE C
%V 14
%N 6
%P 417-424
%@ 1869-1951
%D 2013
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1200310
TY - JOUR
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
Y1 - 2013
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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