基于强化学习的认知雷达目标跟踪波形挑选方法

Waveform Selection Method of Cognitive Radar Target Tracking Based on Reinforcement Learning

认知雷达通过不断与环境互动并从经验中学习,根据获得的知识不断调整其波形、参数和照射策略,以在复杂多变的场景中实现稳健的目标跟踪,其波形设计在提高跟踪性能方面一直备受关注。该文提出了一种用于跟踪高机动目标的认知雷达波形选择框架,该框架考虑了恒定速度(CV)、恒定加速度(CA)和协同转弯(CT)模型的组合,在该框架的基础上设计了基于准则优化(CBO)和熵奖励Q学习(ERQL)方法进行最优波形选择。该方法将雷达与目标集成到一个闭环中,发射波形随目标状态的变化实时更新,从而达到对目标的最佳跟踪性能。数值结果表明,与CBO方法相比,所提出的ERQL方法大大减少了获取最优波形的处理时间,并实现了与CBO相近的跟踪性能,相比于固定参数(Fixed-P)方法,极大地提高了机动目标的跟踪精度。

Based on the obtained knowledge through ceaseless interaction with the environment and learning from the experience,cognitive radar continuously adjusts its waveform,parameters,and illumination strategies to achieve robust target tracking in complex and changing scenarios.Its waveform design has been receiving attention to improve tracking performance.In this paper,we propose a novel framework of cognitive radar waveform selection for the tracking of high-maneuvering targets.The framework considers the combination of Constant Velocity(CV),Constant Acceleration(CA),and Coordinate Turn(CT)motions.We also design Criterion-Based Optimization(CBO)and Entropy Reward Q-Learning(ERQL)methods to perform waveform selection based on this framework.To provide the optimum target tracking performance,it merges the radar and target into a closed loop,updating the broadcast waveform in real-time as the target state changes.The suggested ERQL technique achieves about the same tracking performance as the CBO while using much less processing time than the CBO,according to numerical results.The proposed ERQL method significantly increases the tracking accuracy of moving targets as compared to the fixed parameter approach.