针对传统势概率假设密度(CPHD)滤波算法在杂波率未知的情况下跟踪效果不佳、计算量繁重的问题,提出了一种改进的杂波率未知环境下CPHD滤波算法。该算法首先针对杂波先验未知的情况,提出杂波率未知条件下的CPHD滤波算法,并针对CPHD滤波算法计算复杂的问题,引入最大似然自适应门限,利用进入门限中的量测进行更新步。实验结果表明,算法在降低计算时间的同时,保证了算法在杂波率未知环境下的跟踪性能。

In view of the traditional cardinalized probability hypothesis density (CPHD) filtering algorithm has a poor tracking effect and heavy computation in the case of unknown clutter rate environment, an improved CPHD filtering algorithm under the unknown clutter rate environment is proposed. Based on the unknown condition of clutter rate, the algorithm proposes a CPHD filtering algorithm under the unknown condition of clutter rate, and the maximum likelihood adaptive threshold is introduced to solve the problem of high complexity in the CPHD filtering algorithm, and the updating step is carried out by using the measurement in the threshold. Experimental results show that the algorithm reduces the computation time while ensuring the tracking performance of the algorithm in clutter unknown conditions.