Sensor Management Method Based on Deep Reinforcement Learning in Extended Target Tracking

被引：0
作者：
Zhang, Hong-Yun [1 ]
Chen, Hui [1 ]
Zhang, Wen-Xu [1 ]
机构：
[1] School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2024年 / 50卷 / 07期
基金：
中国国家自然科学基金;
关键词：
deep reinforcement learning (DRL); extended target tracking (ETT); information gain; Sensor management; twin delayed deep deterministic policy gradient (TD3);
D O I：
10.16383/j.aas.c230591
中图分类号：
学科分类号：
摘要：
To solve the problem of sensor management in the optimization of extended target tracking (ETT), this paper proposes a sensor management method based on deep reinforcement learning (DRL) by modeling the extended target based on random matrices model (RMM). First, in the theoretical framework of partially observed Markov decision process (POMDP), a elementary method of sensor management for extended target tracking based on twin delayed deep deterministic policy gradient (TD3) algorithm is presented. After that, the Gaussian Wasserstein distance (GWD) is used to calculate the information gain between the prior probability density and the posterior probability density of the extended target, which is used to comprehensively evaluate the multi-feature estimation information of the extended target, and then the information gain is used as the reward function of TD3 algorithm. Furthermore, the optimal sensor management scheme based on deep reinforcement learning is decided by the derived reward function. Finally, the effectiveness of the proposed algorithm is verified by constructing an extended target tracking optimization simulation experiment. © 2024 Science Press. All rights reserved.
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收藏
页码：1417 / 1431
页数：14
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