基于CFSFDP的箱粒子CPHD扩展目标跟踪

针对当前扩展目标跟踪量测划分方法中,距离划分存在划分数过多、计算复杂度高的问题,本文将密度峰值快速聚类算法CFSFDP (Clustering by Fast Search and Find of Density Peaks)与箱粒子势概率假设滤波器(Box Cardinalized Probability Hypothesis Density filter,Box-CPHD)相结合,提出基于CFSFDP的箱粒子CPHD扩展目标滤波算法.该算法采用CFSFDP进行量测划分,基于量测信息密度的不同可以有效划分区间量测,并剔除杂波量测,然后采用箱粒子CPHD进行预测更新和目标状态估计.仿真实验表明与经典的距离划分方法相比,在箱粒子CPHD扩展目标算法流程中采用CFSFDP进行量测预处理,CFSFDP在达到同等效果的前提下,运行时间明显减少;在剔除杂波之后的高杂波环境下,杂波的变化只影响距离划分的运算时间而不再影响CFSFDP划分,采用CFSFDP处理量测信息可以有效提高运行效率和算法实时性,剔除杂波之后在一定程度上提高了目标位置估计精度.

Box-CPHD Extended Target Tracking Based on CFSFDP

Regarding the problems of excessive divisions and high computational complexity in the current measurement division method of extended target tracking, this study combines the Clustering by Fast Search and Find of Density Peaks (CFSFDP) with the Box-Cardinalized Probability Hypothesis Density (Box-CPHD) filter to propose a Box-CPHD extended target filtering algorithm based on CFSFDP. The algorithm applies CFSFDP to measurement division and it can clearly divide the interval measurement and remove the clutter measurement with the difference in the measurement information density. Then, the Box-CPHD filter is used for prediction update and target state estimation. The simulation experiment shows that in comparison with the classic distance division method, CFSFDP is employed in the measurement preprocessing of the Box-CPHD extended target algorithm. CFSFDP significantly reduces the running time while achieving the same effect, and in the high-clutter environment after clutter removal, the change in clutter only affects the calculation time of distance division but no longer affects the CFSFDP division. The processing of measurement information with CFSFDP can greatly improve the operating efficiency and the real-time performance of the algorithm. After clutter removal, the accuracy of target position estimation is improved to a certain extent.