关键词: 目标跟踪, 扩展目标, 量测集划分, 密度分析, 概率假设密度（PHD）

Abstract:

Extended Target Gaussian Mixture Probability Hypothesis Density（ET-GM-PHD） filter has demonstrated as a promising approach in extended target tracking. However, when multiple targets are closely spaced to each other, state estimation accuracy of the algorithm decreases, which is due to the inability of the Distance Partitioning-Kmeans+（DP-Kmeans++） measurement set partitioning algorithm to output the correct results. To solve this problem, an improved DP-Kmeans++ measurement set partitioning algorithm is proposed, which uses Target Prediction（TP） information to set partition measurement, thus the partitioning accuracy will be improved. Simulation results show that, the proposed partitioning algorithm leads to lower OSPA values of the ET-GM-PHD filter, when targets are closely spaced.

Key words: target tracking, extended target, measurement set partitioning, density analysis, Probability Hypothesis Density（PHD）