Voronoi Progressive Widening for Cognitive Radar Tracking with Large Waveform Libraries

We apply an improved variant of Monte Carlo Tree Search (MCTS), MCTS with Voronoi Progressive Widening (VPW), to cognitive radar tracking. Because cognitive radar systems have unparalleled waveform agility across an immense parameter space, reinforcement learning techniques must deal with large, multi-dimensional action spaces. Prior applications of MCTS are inefficient because they uniformly explore new actions without regards to available information. We demonstrate how a Voronoi partitioning based scheme improves on the exploration of new waveforms leading to better combined tracking performance and radar resource usage in a standard benchmark tracking scenario.