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18 November 2024 Multitarget-tracking airborne method for radar based on a mamba network
Yijin Hao, Penghan Song, Wenjuan Sheng, Jianliang Zhu
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Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134031D (2024) https://doi.org/10.1117/12.3051723
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China
Abstract
In order to solve the problem that the traditional airborne radar multi-target tracking data association algorithm needs to obtain the prior information such as clutter density and target motion model in advance, a multitarget-tracking algorithm about data association based on Mamba network is constructed. The proposed algorithm combines the efficiency of selective state space and hardware-aware algorithms. This paper uses a data-driven approach to learn the mapping relationship between each target and measurement without providing various prior information to solve the problem of associating multiple targets with multiple measurements, and comprehensively considers the problems of missed detection and false alarm. Simulation results show that the optimal sub-pattern assignment (OSPA) distance error of the proposed algorithm is smaller, and it has better performance than the algorithm based on the Bi-LSTM network and the classic data association algorithm in the tracking experiment.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yijin Hao, Penghan Song, Wenjuan Sheng, and Jianliang Zhu "Multitarget-tracking airborne method for radar based on a mamba network", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134031D (18 November 2024); https://doi.org/10.1117/12.3051723
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KEYWORDS
Detection and tracking algorithms
Data modeling
Clutter
Education and training
Radar
Matrices
Target detection
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