A Nonlinear Waveform Selection Method for Cognitive Radar Target Tracking Based on Reinforcement Learning

Cognitive radar automatically adjusts its waveform via ceaseless interaction with the environment and learning from the experience. The waveform development of cognitive radar has been attracting much attention in improving tracking performance. In this paper, we propose an intelligent radar target tracking strategy based on variable nonlinear frequency modulated waveforms (NLFM). The strategy considers the combination of constant velocity (CV), constant acceleration (CA), and constant turning (CT) motion for high maneuvering targets. A library of NLFM is constructed and the entropy reward Q-Learning (ERQL) method is designed to perform joint waveform parameters selection. It merges the radar and target into a closed loop to provide the optimum target tracking performance, updating the waveform in real-time as the target state changes. Numerical results show that the tracking performance of our proposed method is much better than that of the linear frequency modulated waveform (LFM) pure parameter selection method.