A Task Scheduling for Digital Array Radar Based on Reinforcement learning

Target tracking is the key technology of the Digital Array Radar (DAR). The adaptive radar task scheduling interval can exert the advantage of digital array beams, significantly improving the target tracking performance. This article introduces the adaptive task scheduling interval technology in the task scheduling algorithm. This technology is an adaptive task scheduling interval scheme based on Reinforcement Learning (RL), realizing better resource scheduling through priority competition mechanisms. The adaptive task scheduling interval can not only timely compensate for the target movement status information during the tracking process but also increase the measurement quantity to improve the stability of tracking maneuverability. Due to the limited radar resources, reducing the tracking task scheduling interval restricts the system tracking capacity and the ability to complete other functions. Adaptive scheduling interval technologies should maintain a certain degree of balance between search and tracking. Finally, the adaptive scheduling interval control technology is simulated and verified.