A Cognitive Tracking Radar Using Continuous Space Monte Carlo Tree Search

This paper presents the application of a continuous space Monte Carlo tree search (MCTS) technique to cognitive tracking radar. Previous cognitive tracking radar implemen-tations have used approximate dynamic programming (ADP) techniques like receding horizon (RH) and policy rollout to intel-ligently select waveforms. We show how MCTS combines features of both RH and rollout to produce superior tracking performance while also conserving radar resources in a benchmark tracking simulation.