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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
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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     
Received: 05 November 2012      Published: 04 June 2013
CLC:  TP391  
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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/10.1631/jzus.C1200310     OR     http://www.zjujournals.com/xueshu/fitee/Y2013/V14/I6/417


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

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 
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