Intelligent Suppression of Interferences Based on Reinforcement Learning

This article aims to investigate intelligent strategies of interference suppression for radar systems in the background of complex electromagnetic interferences. At the modeling stage, an interactive loop is established exploiting the interaction between the radar and the environment for interference suppression based on reinforcement learning. Specifically, the mappings from the interference suppression to the reinforcement learning, including the interference state set, the method set, evaluation criteria of interference suppression in different domains, and the principle of interference substate transformation, have been established. In this respect, two algorithms, including the Retroactive-Q (R-Q) learning and Retroactive-Deep Q Network (R-DQN), are developed by introducing a backtracking Q-value, which links the evaluations in each time step of a training round. At the analysis stage, the selection probabilities of the optimal implementation sequence for interference suppression are studied, and comparisons among the devised R-Q learning, R-DQN, conventional Q learning, and DQN are carried out in terms of output Q-values. Numerical results corroborate the effectiveness and robustness of the considered suppression strategies in diverse scenarios.