In this paper, the problem of unknown clutter prior information for multiple extended targets is considered by introducing the network flow theory. To achieve multiple extended target tracking using this technique, a hierarchical clustering algorithm is developed to segment the measurement set. In particular, we build a multiple extended target (MET) network flow model with a multi-constrained minimum cost optimization function, as well as use the A-star search algorithm to obtain optimal associated tracks. The proposed algorithm is compared with an extended target Gaussian mixture probability hypothesis density (ET-GM-PHD) filter, and the simulation results show that the proposed method has improved estimation and tracking performance.
Multi-extended Target Tracking Algorithm with Unknown Clutter Information
Lect. Notes Electrical Eng.
International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021
Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021) ; Chapter : 196 ; 1986-1996
2022-03-18
11 pages
Article/Chapter (Book)
Electronic Resource
English
Multi-extended Target Tracking Algorithm with Unknown Clutter Information
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