Multiple hypothesis tracking based on the Shiryayev sequential probability ratio test

Abstract

To date, Wald sequential probability ratio test (WSPRT) has been widely applied to track management of multiple hypothesis tracking (MHT). But in a real situation, if the false alarm spatial density is much larger than the new target spatial density, the original track score will be very close to the deletion threshold of the WSPRT. Consequently, all tracks, including target tracks, may easily be deleted, which means that the tracking performance is sensitive to the tracking environment. Meanwhile, if a target exists for a long time, its track will have a high score, which will make the track survive for a long time even after the target has disappeared. In this paper, to consider the relationship between the hypotheses of the test, we adopt the Shiryayev SPRT (SSPRT) for track management in MHT. By introducing a hypothesis transition probability, the original track score can increase faster, which solves the first problem. In addition, by setting an independent SSPRT for track deletion, the track score can decrease faster, which solves the second problem. The simulation results show that the proposed SSPRT-based MHT can achieve better tracking performance than MHT based on the WSPRT under a high false alarm spatial density.

创新点

多假设跟踪(MHT)的航迹管理通常采用Wald序贯概率比检测(WSPRT), 当场景中杂波密度远高于新生目标密度时, 存在真实目标航迹易被误删除的问题。同时, 由于长时间存在的目标航迹有较高的得分, 应用WSPRT将使对应航迹在目标消失很长时间之后才能被删除。为解决上述问题, 本文使用贝叶斯框架下的Shiryayev序贯概率比检测(SSPRT)替代WSPRT进行MHT的航迹管理, 通过引入假设转移概率并设置两个独立的SSPRT分别进行航迹的确认和删除。仿真结果表明, 所提出的基于SSPRT的MHT算法在高杂波密度的条件下, 能够更快地起始真实目标航迹, 同时更快地删除虚假航迹。