Adaptive Clutter Intelligent Suppression Method Based on Deep Reinforcement Learning

Author:

Cheng Yi12,Su Junjie1,Xiu Chunbo1,Liu Jiaxin1

Affiliation:

1. School of Control Science and Engineering, Tiangong University, Tianjin 300387, China

2. Tianjin Key Laboratory of Intelligent Control for Electrical Equipment, School of Control Science and Engineering, Tiangong University, Tianjin 300387, China

Abstract

In the complex clutter background, the clutter center frequency is not fixed, and the spectral width is wide, which leads to the performance degradation of the traditional adaptive clutter suppression method. Therefore, an adaptive clutter intelligent suppression method based on deep reinforcement learning (DRL) is proposed. Each range cell to be detected is regarded as an independent intelligence (agent) in the proposed method. The clutter environment is interactively learned using a deep learning (DL) process, and the filter parameter optimization is positively motivated by the reinforcement learning (RL) process to achieve the best clutter suppression effect. The suppression performance of the proposed method is tested on simulated and real data. The experimental results indicate that the filter notch designed by the proposed method is highly matched with the clutter compared with the existing adaptive clutter suppression methods. While suppressing the clutter, it has a higher amplitude-frequency response to signals at non-clutter frequencies, thus reducing the loss of the target signal and maximizing the output signal-to-clutter and noise rate (SCNR).

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

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