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Front. Inform. Technol. Electron. Eng.  2013, Vol. 14 Issue (6): 417-424    DOI: 10.1631/jzus.C1200310
    
A multiple maneuvering targets tracking algorithm based on a generalized pseudo-Bayesian estimator of first order
Shi-cang Zhang, Jian-xun Li, Liang-bin Wu, Chang-hai Shi
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Aviation Key Laboratory of Science and Technology on AISSS, AVIC Radar and Avionics Institute, Wuxi 214063, China
A multiple maneuvering targets tracking algorithm based on a generalized pseudo-Bayesian estimator of first order
Shi-cang Zhang, Jian-xun Li, Liang-bin Wu, Chang-hai Shi
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Aviation Key Laboratory of Science and Technology on AISSS, AVIC Radar and Avionics Institute, Wuxi 214063, China
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摘要： 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.
关键词： Gaussian mixture PHD filter;  Jump Markov system;  Generalized pseudo-Bayesian estimator of first order (GPB1);  Multi-target tracking    
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.
Key words: Gaussian mixture PHD filter    Jump Markov system    Generalized pseudo-Bayesian estimator of first order (GPB1)    Multi-target tracking
收稿日期: 2012-11-05 出版日期: 2013-06-04
CLC:  TP391  
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Shi-cang Zhang
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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. Front. Inform. Technol. Electron. Eng., 2013, 14(6): 417-424.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1200310    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2013/V14/I6/417

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