A Practical Reinforcement Learning Framework for Automatic Radar Detection

doi:10.12142/ZTECOM.202303004

Abstract

Abstract:

At present, the parameters of radar detection rely heavily on manual adjustment and empirical knowledge, resulting in low automation. Traditional manual adjustment methods cannot meet the requirements of modern radars for high efficiency, high precision, and high automation. Therefore, it is necessary to explore a new intelligent radar control learning framework and technology to improve the capability and automation of radar detection. Reinforcement learning is popular in decision task learning, but the shortage of samples in radar control tasks makes it difficult to meet the requirements of reinforcement learning. To address the above issues, we propose a practical radar operation reinforcement learning framework, and integrate offline reinforcement learning and meta-reinforcement learning methods to alleviate the sample requirements of reinforcement learning. Experimental results show that our method can automatically perform as humans in radar detection with real-world settings, thereby promoting the practical application of reinforcement learning in radar operation.

Key words: meta-reinforcement learning, radar detection, reinforcement learning, offline reinforcement learning

YU Junpeng, CHEN Yiyu. A Practical Reinforcement Learning Framework for Automatic Radar Detection[J]. ZTE Communications, 2023, 21(3): 22-28.

Figures/Tables 5

Figure 1 Framework of reinforcement learning

Figure 2 Environment interaction framework

Figure 3 Process of learning framework

Figure 4 Network structure of the agent

Table 1 Online testing results

Test	Random Policy	Proposed	Experts
Trial 1	-9.24	24.32	26.14
Trial 2	12.78	28.77	29.33
Trial 3	6.34	25.38	30.85
Average	3.29	26.16	28.77

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