基于ET-GMPHD算法的编队目标跟踪方法

doi:10.3969/j.issn.1001-506X.2020.11.04

Abstract

Abstract:

To solve the tracking problem of formation targets in low detection environment, a formation target tracking method based on the extended target Gaussian mix probability hypothesis density (ET-GMPHD) algorithm is proposed. Firstly, the Gaussian term is trimmed and extrapolated, and Jensen-Shannon (JS) divergence is used to measure the degree of similarity between the state estimate and the extrapolated value at the next moment, and to reflect whether there are target loss and guarantee the true targets are not trimmed, which solves the problem of inaccurate tracking results due to missed detection of the target. Secondly, combined with the cyclic threshold aggregation method, the state estimation of the overall formation is obtained, which eliminates the influence of excessive state values in the estimated state set. Finally, simulation experiments show that the method can effectively track with a very low detection probability and has a good tracking performance.

Key words: formation target, low detection, JS divergence, extended target Gaussian mix probability hypothesis density (ET-GMPHD) algorithm, cyclic threshold aggregation

CLC Number:

TN953

Wei XIONG, Xiangqi GU, Congan XU, Yanbiao HAO. Formation target tracking method based on ET-GMPHD algorithm[J]. Systems Engineering and Electronics, 2020, 42(11): 2434-2440.

Figures/Tables 15

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算法	检测概率
算法	P_{D, k}=0.7	P_{D, k}=0.5	P_{D, k}=0.3
C-GMPHD	71.685	73.074	80.815
ET-GMPHD	71.570	72.993	78.258
JC-ET-GMPHD	71.086	71.668	73.036

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Formation target tracking method based on ET-GMPHD algorithm

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