李少军, 王宏, 柴天佑. 基于t-分布粒子滤波器的目标跟踪[J]. 机器人, 2006, 28(6): 598-604.
引用本文: 李少军, 王宏, 柴天佑. 基于t-分布粒子滤波器的目标跟踪[J]. 机器人, 2006, 28(6): 598-604.
LI Shao-jun, WANG Hong, CHAI Tian-you. A t-Distribution-Based Particle Filter for Target Tracking[J]. ROBOT, 2006, 28(6): 598-604.
Citation: LI Shao-jun, WANG Hong, CHAI Tian-you. A t-Distribution-Based Particle Filter for Target Tracking[J]. ROBOT, 2006, 28(6): 598-604.

基于t-分布粒子滤波器的目标跟踪

A t-Distribution-Based Particle Filter for Target Tracking

  • 摘要: 针对贝叶斯跟踪中目标状态的预测分布和后验分布,利用序列蒙特卡洛方法,基于多变量t-分布提出了一种新的粒子滤波算法,称之为t-分布粒子滤波器.为了根据样本估计目标状态的概率分布,提出了一种新的ECME算法,并嵌入到t-分布粒子滤波器中.理论分析表明,在t-分布条件下,t-分布粒子滤波器是在样本数量上的渐近最优估计器.在机动目标跟踪实验中,比较了t-分布粒子滤波器、无色卡尔曼滤波(Unscented Kalman filter)及自助式粒子滤波器(Bootstrap particle filters)的跟踪精度.

     

    Abstract: For the predictive distribution and posterior distribution problem of target states in Bayesian tracking,a new particle filter,called the Student-t distribution Particle Filter(SPF),is developed based on multivariate student-t distributions by using sequential Monte Carlo methodology.To estimate probability density function(PDF) of the target state based on samples,a new Expectation Conditional Maximization Either(ECME) algorithm is introduced and embedded in the SPF.Under the student-t distribution assumption,it is shown theoretically that the SPF is asymptotically optimal in terms of the number of particles.In the tracking of maneuvering target,the performances of SPF,unscented Kalman filter(UKF) and the bootstrap particle filter(SIR) are compared in terms of accuracy.

     

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