Applying Deep-Q Networks to Target Tracking to Improve Cognitive Radar

In this paper we address radar-communication coexistence by modeling the radar environment as a Markov Decision Process (MDP), and then apply Deep-Q Learning to optimize radar performance. The radar environment includes a single point target and a communications system that will potentially interfere with the radar. We demonstrate that the Deep-Q Network (DQN) we construct is able to successfully avoid interfering with the communication system when necessary. We also show that the DQN method outperforms previous methods in terms of memory and handling new situations.