基于动态RCS的无人机航迹实时规划

Real-time programming method for flight path of unmanned vehicle based on dynamic RCS

传统的无人机航迹规划主要采用仅考虑无人机与雷达距离的简化雷达威胁模型,未充分考虑无人机雷达散射截面RCS(Radar Cross-Section)随自身姿态角改变而产生的动态变化.据此,提出了无人机周向动态RCS模型,并建立了综合考虑无人机动态RCS与雷达距离的探测概率模型,利用遗传算法进行了基于动态RCS的航迹实时规划,计算结果与传统航迹规划结果进行了对比.仿真结果表明该模型的可行性和有效性,能充分利用无人机自身的优势规避威胁,满足无人机的航迹实时规划的要求.

Simplified radar detection models are applied in conventional flight path programming of unmanned vehicle, in which only the distance between unmanned vehicle and radar has been taken into account, but the dynamic variation of radar cross-section （RCS） with attitude angle of unmanned vehicle has not been considered. New dynamic RCS model was presented to characterize the RCS distribution nature of unmanned vehicle, radar＇s detection probability model was developed to involve dynamic RCS and the distance between radar and unmanned vehicle. From genetic algorithm （GA） and dynamic RCS, real-time programming for flight path of unmanned vehicle was conducted and compared with conventional models. From verification examples, it is demonstrated that the models presented are feasible and valid to avoid the detection of radar.