一种双无人机协同跟踪地面移动目标方法

针对通信延时情况下双无人机协同跟踪地面移动目标问题进行研究, 构建了基于分布式遗传算法和滚动时域优化结合的目标跟踪航迹规划算法模型。考虑到通信延时会增加目标状态信息数据融合时的误差, 导致无人机跟踪任务效果变差, 结合递推最小二乘滤波和加权最小二乘估计设计了融合方法, 来融合处理目标状态信息; 考虑到无人机对目标的观测效果与未来时刻的目标状态信息密切相关, 采用递推最小二乘滤波预测目标的状态信息, 结合分布式遗传算法和滚动时域优化设计了双无人机目标跟踪航迹规划算法。适应度函数考虑了无人机和目标之间的距离、无人机之间的通信距离、无人机之间的通信角度。仿真结果表明：该协同跟踪方法能够较好地完成跟踪任务; 与一架无人机跟踪相比误差明显减小, 并且可以减小通信延时带来的跟踪误差。

Cooperative ground moving target track method usingtwo unmanned aerial vehicles

This paper investigated the cooperative tracking of a ground moving target using two unmanned aerial vehicles under communication delay, and constructed an algorithm model. Considering the communication delay, it proposed a data fusion method combined with the recursive least squares filtering and the weighted least squres. Then it built the recursive least squares filtering for the prediction of target states, designed a trajectory planning algorithm based on receding horizon and the distributed genetic algorithm to implement the task of cooperative target tracking using two unmanned aerial vehicles. The fitness function consisted of several components including the distance of vehicles and target, the communication distance of vehicles, the communication angle of vehicles. Simulation studies show the cooperative track algorithm can implement the track task well; compared to one vehicle track the target, the position error decreases obviously, the position error during to the communication delay can decrease.