基于粒子群算法的低轨重点卫星雷达协同探测任务规划方法

Task Scheduling Method for Collaborative Observation of High-threat LEO Satellites Using Radars Based on PSO Algorithm

为提升定轨、成像、目标特性测量等多种模式下的观测效率,基于粒子群算法提出了一种低轨重点卫星雷达观测任务规划方法。相较基于优先级的任务规划方法,文中方法可对所有卫星按给定观测频次无丢失观测,满足雷达视场、时间窗口、任务切换等约束。使用雷达仰角、斜距、雷达截面积构建权值对基于观测时长的目标函数进行修改,并使用粒子群优化算法求解最大总观测时长或最快完成时间。仿真场景为两部假想地面雷达协同对30颗低轨重点卫星做24 h~72 h观测。结果表明,所有卫星无丢失观测,最大总观测时长和最快完成时间分别为18622 s和15 h 16 min(场景时间:24 h)。使用带有权值的目标函数可获得平均观测仰角、斜距的改善,而总观测时长基本一致。

In order to improve the observation efficiency of orbital determination,imaging,target characteristic measurement and so on,a task scheduling method is proposed for radar observation of high-threat satellites situated in low earth orbit(LEO).Compared with the priority-oriented scheduling method,this method enables all satellites to be observed with a given frequency,satisfying constraints such as radar′s field of view,time window,transition time,etc.The factors of radar detection such as elevation angle,slant,and radar cross-section are utilized to construct the weight when defining the objective function based on time length of observation.Particle swam optimization(PSO)algorithm is employed to achieve the maximum total time length or quickest accomplishment of observation.Simulation is conducted to verify the effectiveness of the proposed method.Two notional ground radars are used to observe 30 high-threat LEO satellites for 24 h~72 h.Results show that:all satellites are observed without missing and under the given conditions,and the maximum total time length and quickest accomplishment of observation are 18622 s and 15 h 16 min,respectively(scenario time:24 h).Furthermore,when using the weighted objective function,it tends to obtain a larger elevation angle and smaller slant during the whole observation process,while the total time length of observation is basically consistent with that of using the non-weighted one.