多扩展目标滤波器的量测集划分算法

A Measurement Set Partitioning Algorithm for Extended Target Gaussian-mixture Probability Hypothesis Density Filter

针对不同扩展目标产生的量测密度差别较大时的量测集划分问题,为扩展目标概率假设密度(PHD)滤波器提出了一种基于共享最近邻(SNN)相似度的量测集划分算法。量测间的SNN相似度可体现量测在量测空间局部分布情况,考虑了量测周围的量测信息,因此提出的SNN相似度划分法能够较好地划分量测密度差别较大的量测集,进而提高了扩展目标的跟踪性能,且基于提出的划分算法的PHD滤波器计算量也所减少。

The partitioning method based on distance does not work well when the densities of measurements from different extended targets are very different, and thus reduce the performance of the extended target probability hypothesis density （PHD） filter. Based on the share nearest neighbors（SNN） similarity, this paper presents a measurement set partitioning approach, which could work well in the situation that the densities of measurements from different extended targets are very different and further could enhance the tracking performance of the filter based on the proposed method. The SNN similarity that incorporates the neighboring measurement information is in- troduced instead of the distance between measurements used in the measurement set partitioning, and thus it is rela- tively insensitive to variation in measurement density. Although calculating the SNN similarity consume some time, the resulting PHD filter based on the proposed partitioning approach does not cause more computational burden due to the lesser number of the resulting partitions. Especially in high clutter scenarios, a significant reduction in com- putational complexity can be achieved. Simulation results demonstrate the superiority of the filter based on the pro- posed partitioning approach.