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

作　　者：Shi-cang ZHANG Jian-xun LI Liang-bin WU Chang-hai SHI 

机构地区：[1]School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University [2]Aviation Key Laboratory of Science and Technology on AISSS,AVIC Radar and Avionics Institute

出　　处：《Journal of Zhejiang University-Science C(Computers and Electronics)》2013年第6期417-424,共8页浙江大学学报C辑（计算机与电子（英文版）

基　　金：Project supported by the National Natural Science Foundation of China(Nos.61175008,60935001,and 61104210);the Aviation Foundation(No.20112057005);the National Basic Research Program(973) of China(No.2009CB824900)

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.We describe the design of a multiple maneuvering targets tracking algorithm under the framework of Oaussian 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 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]