Constrained Deep Reinforcement Learning for Cognitive Radar Resource Management
This work provides a novel reinforcement learning-based solution for resource management in cognitive radar systems, which is important for improving tracking and scanning performance under real-time constraints.
The paper addresses time allocation for multi-target tracking and scanning in cognitive radar under a time budget constraint, using a constrained deep reinforcement learning framework. The proposed method autonomously allocates more time to high-importance tracking tasks while respecting the time budget, outperforming heuristic and optimization-based approaches.
In this paper, multi-target tracking and scanning are considered in a radar system operating in the track-while-scan mode. Specifically, time allocation for radar scanning and tracking of multiple maneuvering targets under a time budget constraint is addressed, aiming to jointly optimize the performance of both tracking and scanning in a cognitive radar. We first present the details of the model for tracking and scanning and formulate the time management task as a constrained optimization problem. Subsequently, we design a \gls{cdrl} framework to find the time allocation strategy for the problem. In the proposed \gls{cdrl} framework, the parameters of the neural networks and the dual variable are learned simultaneously. The deep deterministic policy gradient (DDPG) algorithm is introduced to tackle continuous action space and its performance is compared with deep Q-learning, heuristic approaches, and an optimization-based approach. Numerical results show that the radar with the proposed \gls{cdrl} framework 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.