中文摘要：

针对大量杂波环境下数量变化的纯角度多目标航迹关联跟踪问题,提出一种新的基于Rao-Blackwellized粒子采样（RBPF）航迹关联的高斯混合概率假设密度（GMPHD）滤波算法.算法首先利用GMPHD在每时刻对多个目标组成的随机集合进行估计;然后利用基于随机有限集的RBPF对GMPHD所得到的目标集合进行检测和关联,有效解决GMPHD算法中无法进行多目标航迹识别的弊端;最后通过对所有粒子的融合完成航迹区分和估计.实验结果表明,提出方法比起目前经典的随机集Label-PHD关联跟踪算法,可以更有效的对数量未知的多目标航迹进行区分和关联估计,同时算法的跟踪性能及稳定性要好于Label-PHD算法.

英文摘要：

Due to the difficulty in association and estimation of multi-target tracks in the presence of data association uncertainty,clutter,noise and miss-detection.In this paper,a novel data association probability hypothesis density（PHD） filter for multi-target tracking based on Rao-Blackwellized particle filter（RBPF） algorithm is proposed.Firstly,the Gaussian mixture probability hypothesis density（GMPHD） filter has been proposed to estimate the set of all targets at every time step.Secondly,the data-association functionalities of RBPF can be incorporated with the PHD filter to produce the track-valued estimates of individual targets.Simulation results show that the proposed algorithm is more robust and accurate than Label-PHD algorithm which is very prevalent in the PHD tracking domains,also the proposed algorithm can estimate and distinguish each target more effective.