Tracking Method without Prior Information when Multi-group Targets Appear Successively

Wei XIONG; Xiangqi GU; Congan XU; Yaqi CUI

doi:10.11999/JEIT190508

Volume 42 Issue 7

Jul. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(7): 1619-1626

Wei XIONG, Xiangqi GU, Congan XU, Yaqi CUI. Tracking Method without Prior Information when Multi-group Targets Appear Successively[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1619-1626. doi: 10.11999/JEIT190508

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Wei XIONG, Xiangqi GU, Congan XU, Yaqi CUI. Tracking Method without Prior Information when Multi-group Targets Appear Successively[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1619-1626. doi: 10.11999/JEIT190508

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Tracking Method without Prior Information when Multi-group Targets Appear Successively

doi: 10.11999/JEIT190508

Institute of Information Fusion, Naval Aeronautical University, Yantai 264001, China

Funds: The National Natural Science Foundation of China (91538201, 61790550)

Received Date: 2019-07-08
Rev Recd Date: 2020-03-22

Available Online: 2020-04-09

Publish Date: 2020-07-23

Abstract

Abstract

Considering the problem of multi-group maneuvering target tracking, a fast tracking method based on Interactive Multiple Maneuvering Gaussian Mixture Probability Hypothesis Density (IMM-GM-PHD) algorithm is proposed. Firstly, based on the completion of the IMM-GM-PHD algorithm prediction process, the density detection mechanism is added, and the correlation domain is used to select effective measurement for all predicted Gaussian components, and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is combined to detect whether a new formation target appears. Secondly, based on the completion of the state update of the IMM-GM-PHD algorithm, the update of the model probability is completed by updating the composition of the Gaussian component. Finally, in the process of state estimation optimization, combined with the characteristics of formation targets, the similarity discrimination technique is added, and the Jensen-Shannon (JS) divergence is used to measure the similarity between Gaussian components, and the Gaussian components without similar components are eliminated, and the estimation results are further optimized. The simulation results show that the proposed algorithm can track multi-group maneuvering targets quickly and effectively, and has better tracking performance.

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References(20)

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