基于FOA的无人机协同态势感知搜寻模型构建及应用

Construction and application of a cooperative situational awareness combat model for unmanned aerial vehicles based on FOA

无人机协同态势感知搜寻任务规划问题是当前无人机应用研究领域的关注重点。基于现实无人机搜寻任务统计的大数据概率搜寻热图,概括总结出区域定点搜寻、大范围机动搜寻和机动避让搜寻3类搜寻目标机动模式,采取大数据增量计算3类情形下的无人机协同搜索识别概率,基于果蝇优化算法对应构建了“效率优先”“中心辐散”和“向心辐合”搜索策略的无人机协同态势感知FOA搜寻模型,得到无人机协同搜索到搜寻机动目标的最高概率分别为66.82%、86.72%和81.48%,并利用博弈论思想对无人机协同搜索策略和搜寻机动目标对抗避让策略进行了辨析。该模型具有较强适应性,为无人机协同态势感知搜寻任务规划问题提供了一定技术支撑。

The problem of drone collaborative situational awareness task planning is currently a focus of attention in the field of drone military research.Based on the big data reconnaissance heat map of the realistic military reconnaissance mission statistics,this study summarizes three types of reconnaissance target maneuvering modes:regional fixed-point reconnaissance,large scale mobile reconnaissance and mobile avoidance reconnaissance.The big data incremental computing is adopted to calculate the recognition probability of UAV(Unmanned Aerial Vehicle)cooperative search under three types of situations.Based on the fruit fly optimization algorithm(FOA),an unmanned aerial vehicle collaborative situational awareness FOA combat model was constructed with search strategies of"efficiency first","central divergence"and"centripetal convergence".The highest probabilities of drone cooperative search for reconnaissance maneuvering targets are 66.82%,86.72%and 81.48%respectively.Based on game theory,the cooperative search strategy of UAV and the countermeasure and avoidance strategy of reconnaissance maneuvering target are analyzed.This model has strong adaptability and provides certain technical support for the planning of unmanned aerial vehicle collaborative situational awareness tasks.