一种状态与杂波相关条件下的GM-CPHD算法

GM-CPHD Filter with State-dependent Clutter

对许多传感器而言,所观测到的杂波更容易集中在目标所处区域。此时,杂波不再是均匀分布,杂波的分布与真实目标所处状态相关,这与传统滤波算法中的假设不同。在此条件下,传统多目标跟踪算法的跟踪精度和实时性会受到很大影响。针对该问题,提出一种状态与杂波相关条件下的GM-CPHD滤波算法。对状态与杂波之间的相关性进行建模;根据整个监视区域的杂波分布重新计算杂波强度,并将其应用于滤波更新过程中;为降低时间复杂度,采用自适应椭球门限在算法更新步骤之前对量测集合进行预处理,使用落入门限内的量测集合进行更新步骤的运算。仿真结果表明,在状态与杂波相关条件下,本文算法较传统算法具有更好的滤波精度以及更低的时间复杂度。

For many sensors the clutters observed are easier to concentrate in the area around targets, namely the state-dependent clutter, which is different from the known clutter distribution in traditional filtering algorithms. So, the accuracy the real-time performance and of traditional multi-target tracking will be greatly degraded. To solve this problem, a kind of GM-CPHD filter with state-dependent clutter was proposed. The relationship between clutter and state was modeled. The clutter intensity was recalculated according to the distribution of clutter in the whole surveillance area and applied to update process,＂ at the same time, in order to reduce the time complexity, an adaptive gating strategy was adopted to make a pretreatment, that is, only the measurements which fell into a threshold were used in update process. The simulation results show, in the environment of state-dependent clutter, the proposed algorithm has better filtering accuracy and lower time complexity.