Cognitive Radar Target Tracking Using Intelligent Waveforms Based on Reinforcement Learning

Cognitive radar (CR) automatically improves itself via ceaseless interaction with the environment and learning from the experience. It continuously adjusts its waveform and parameters and illuminates strategies based on obtained knowledge to achieve robust target tracking despite complex and changing scenarios. Waveform development for CR has attracted sustaining attention in promoting tracking performance. In this article, we propose a novel framework of CR waveform selection for the tracking of high maneuvering targets in a cluttered environment with an interactive multimodel (IMM) probabilistic data association (PDA) algorithm. Based on this framework, criterion-based optimization (CBO) and entropy-rewarded Q-learning (ERQL) methods are designed to perform waveform selection, which is divided into pure parameters selection and joint selection of waveforms and parameters. This method integrates the radar target into a closed loop and realizes the real-time update of the transmitted waveform with the change of the target state, to achieve the best tracking performance of the target. The simulations performed on radar target tracking have demonstrated that the proposed ERQL method outperforms the existing method in both time complexity and tracking accuracy. Furthermore, field experiments have confirmed that the ERQL method is more effective in target tracking than the existing method.