基于SMC-PHD滤波的显式航迹维持算法

An Explicit Track Continuity Algorithm for SMC-PHD Filter

多目标跟踪的实时性、目标的状态提取与航迹连续的正确率受杂波、漏检、目标近距离这些因素的干扰。为了解决这些问题,提出一种改进的SMC-PHD滤波器。首先,基于2个"一对一"准则,提出粒子贴标签方法和粒子簇权值重置机制,可屏蔽高先验密度区域杂波以及检测的不确定性对多目标状态估计及其数目的干扰。其次,将多目标状态提取转换为多个可提供身份标识的单目标状态提取,得到显式的航迹维持。此外,提出一种新颖的粒子重采样方法,可减少近距离目标对彼此后验信息的干扰。仿真验证了提出的显式航迹维持算法的有效性。与基本的SMC-PHD滤波器相比,显著地提高了多目标跟踪的性能,包括实时性与精度。

In multi-target tracking, the real-time performance, state-estimates accuracy, and track continuity are ater, missed detection, and closely spaced targets. To solve these problems, an improved sequential Monte Carlo implementation（ SMC） of the probability hypothesis density （ PHD） filter is proposed. First, based obeling approach and weight redistribution scheme for particle cloud are proposed to shield ahigh prior density region and the detection uncertainty on the estimation. Second,the mmultiple single-estimate extractions, which can provide the identity of the individual target; thus, explicit track maintenance canbe obtained. Finally, a novel resampling scheme is proposed to reduce the effects of closely spaced targets on individual posteriorinformation. The results of numerical experiments demonstrate that the proposed approach can aand better performance compared to the basic SMC-PHDfilter, in terms of faster processing speed and superior estimation accuracy.