Reinforcement Learning for Adaptable Bandwidth Tracking Radars

In this article,
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Portions of this work were presented at the 2018 IEEE Radar Conference [1]. we examine a cognitive radar that must coexist with communication systems. Specifically, we model a cognitive radar's adaptation to the communication system as a Markov decision process (MDP) and then apply reinforcement learning to solve the resulting optimization problem. More specifically, we learn the environment model and then apply policy iteration to determine the optimal policy. The radar environment consists of a single moving target and a communication system that uses the same bands as the radar. The communication system is modeled using several different transmission behaviors: constant, intermittent, triangular frequency sweep, sawtooth frequency sweep, frequency hop, pseudorandom frequency hop, and direction-dependent interference. We demonstrate how the MDP framework and reinforcement learning can be used to help the radar determine actions (i.e., transmission strategies) to maximize its own performance.