Sensor Management Method Based on Deep Reinforcement Learning in Extended Target Tracking
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摘要: 针对扩展目标跟踪(Extended target tracking, ETT)优化中的传感器管理问题, 基于随机矩阵模型(Random matrices model, RMM)建模扩展目标, 提出一种基于深度强化学习(Deep reinforcement learning, DRL)的传感器管理方法. 首先, 在部分可观测马尔科夫决策过程(Partially observed Markov decision process, POMDP)理论框架下, 给出基于双延迟深度确定性策略梯度(Twin delayed deep deterministic policy gradient, TD3)算法的扩展目标跟踪传感器管理的基本方法; 其次, 利用高斯瓦瑟斯坦距离(Gaussian Wasserstein distance, GWD)求解扩展目标先验概率密度与后验概率密度之间的信息增益, 对扩展目标多特征估计信息进行综合评价, 进而以信息增益作为TD3算法奖励函数的构建; 然后, 通过推导出的奖励函数, 进行基于深度强化学习的传感器管理方法的最优决策; 最后, 通过构造扩展目标跟踪优化仿真实验, 验证了所提方法的有效性.
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关键词:
- 传感器管理 /
- 扩展目标跟踪 /
- 深度强化学习 /
- 双延迟深度确定性策略梯度 /
- 信息增益
Abstract: 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 elliptical extended target tracking optimization simulation experiment. -
图 1 扩展目标跟踪中传感器管理方法的研究框架
Fig. 1 Research framework of sensor management method in extended target tracking
图 4 基于TD3的扩展目标跟踪算法伪代码流程图
Fig. 4 Pseudocode flow chart of elliptical extended target tracking algorithm based on TD3
图 5 传感器控制智能决策训练曲线
Fig. 5 Training curve of intelligent decision-making for sensor control
图 16 群目标轨迹跟踪估计细节放大图
Fig. 16 Estimate detail enlarge image of group target trajectory tracking
图 17 群目标跟踪场景中的传感器控制轨迹对比
Fig. 17 Comparison of sensor control trajectory in group target tracking scenarios
图 18 群目标编队第1次变换跟踪细节放大图
Fig. 18 Group target formation the 1st transformation tracking detail enlarge image
图 19 群目标编队第2次变换跟踪细节放大图
Fig. 19 Group target formation the 2nd transformation tracking detail enlarge image
图 21 基于6种方法的群目标跟踪估计的GWD
Fig. 21 GWD of group target tracking estimation based on 6 methods
表 1 智能体训练参数
Table 1 TD3 agent training parameters
超参数名称 参数值 评论家网络学习率${\alpha _Q}$ 0.001 演员网络学习率${\beta _\mu }$ 0.0001 训练批次大小$N$ 128 经验回放单元${\cal{D}} $容量 $1 \times {10^4}$ 每幕最大时间步 $1 \times {10^3}$ 惯性更新率$\tau $ 0.002 更新频率比$m$ 2 折扣因子$\gamma $ 0.9 
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表 2 6种方法下质心误差的统计均值
Table 2 Statistical average of centroid error under 6 methods
实验方法 质心误差统计均值(m) 对比方法1提升率(%) 方法1 0.5091 — 方法2 0.4732 7 方法3 0.4507 11 方法4 0.4349 15 方法5 0.3974 22 方法6 0.3842 25
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表 3 6种方法下GWD的统计均值
Table 3 Statistical average of GWD under 6 methods
实验方法 GWD统计均值(m) 对比方法1提升率(%) 方法1 1.2848 — 方法2 1.2373 4 方法3 1.1751 9 方法4 1.2042 6 方法5 1.1565 10 方法6 1.0612 17
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表 4 6种方法下质心误差的统计均值
Table 4 Statistical average of centroid error under 6 methods
实验方法 质心误差统计均值(m) 对比方法1的提升率(%) 方法1 0.4902 — 方法2 0.4753 3 方法3 0.4463 9 方法4 0.4455 9 方法5 0.4353 11 方法6 0.4197 14
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表 5 6种方法下GWD的统计均值
Table 5 Statistical average of GWD under 6 methods
实验方法 GWD的统计均值(m) 对比方法1提升率(%) 方法1 20.8413 — 方法2 20.6772 1 方法3 20.0186 4 方法4 20.0284 4 方法5 19.7976 5 方法6 19.2237 8
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