Learning-Based Cognitive Radar Resource Management for Scanning and Multi-Target Tracking

In this paper, scanning and multi-target tracking in a radar system are considered, and adaptive radar resource management is analyzed. In particular, time management in radar scanning and tracking of multiple maneuvering targets subject to budget constraints is studied with the goal to jointly maximize the tracking and scanning performances of a cognitive radar. The constrained optimization of the dwell time allocation to each target is addressed via deep Q-network (DQN) based reinforcement learning. In the proposed constrained deep reinforcement learning (CDRL) algorithm, both the parameters of the DQN and the dual variable are learned simultaneously. Numerical results show that radar can autonomously allocate more time to the tracking task that requires greater attention while providing time for scanning and also constraining the total time budget below the predefined threshold.