面向雷达目标检测性能的多弹协同航迹规划

Multi-missile collaborative path planning for radar target detection performance

基于多弹平台的空间分布式雷达构型决定了杂波距离环位置,直接影响杂波回波能量的强弱,从而影响目标检测性能。为实现多弹协同航迹规划过程中的杂波抑制,提升目标检测性能,基于多智能体深度强化学习算法对多弹航迹进行智能规划,将目标检测性能作为该算法的优化目标,引入能量约束与动力学约束等约束条件,训练得到多弹协同智能航迹规划策略,通过控制多弹飞行过程中的空间分布降低杂波回波能量,实现动态环境下的杂波抑制。通过仿真验证表明,所提算法能有效降低强杂波对目标检测的影响,提高了多弹在飞行过程中的检测性能。

The configuration of spatially distributed radar based on multi-missile platform determines the position of clutter range loop,which affects the performance of target detection.In order to realize clutter suppression and improve the target detection performance in the process of multi-missile cooperative path planning,the multi-missile intelligent path planning is carried out based on multi-agent deep deterministic policy gradient algorithm.The target detection performance is taken as the optimization objective of the algorithm,and some constraints such as energy constraints and dynamic constraints are introduced,the strategy of multi-missile cooperative intelligent path planning is obtained through training of the algorithm.The clutter energy is reduced by controlling the spatial distribution in the process of multi-missile flight,therefore the clutter suppression in dynamic environment is realized.The simulation results show that the proposed algorithm can effectively reduce the influence of strong clutter on target detection and improve the detection performance of multi-missile during flight.