Multifunction Cognitive Radar Task Scheduling Using Monte Carlo Tree Search and Policy Networks
This addresses the challenge of efficient radar task scheduling in overload situations for radar systems, representing an incremental improvement by combining existing techniques for better performance.
The paper tackled the NP-hard problem of task scheduling in multifunction cognitive radar resource management, proposing a machine learning-based method using Monte Carlo tree search and policy networks that achieves near-optimal performance with orders of magnitude lower computational complexity than the optimal branch-and-bound algorithm.
A modern radar may be designed to perform multiple functions, such as surveillance, tracking, and fire control. Each function requires the radar to execute a number of transmit-receive tasks. A radar resource management (RRM) module makes decisions on parameter selection, prioritization, and scheduling of such tasks. RRM becomes especially challenging in overload situations, where some tasks may need to be delayed or even dropped. In general, task scheduling is an NP-hard problem. In this work, we develop the branch-and-bound (B&B) method which obtains the optimal solution but at exponential computational complexity. On the other hand, heuristic methods have low complexity but provide relatively poor performance. We resort to machine learning-based techniques to address this issue; specifically we propose an approximate algorithm based on the Monte Carlo tree search method. Along with using bound and dominance rules to eliminate nodes from the search tree, we use a policy network to help to reduce the width of the search. Such a network can be trained using solutions obtained by running the B&B method offline on problems with feasible complexity. We show that the proposed method provides near-optimal performance, but with computational complexity orders of magnitude smaller than the B&B algorithm.