Research On Multi-UAV Cooperative Target Tracking Strategy In Dynamic Environment

Unmanned aerial vehicles（UAVs）need to coordinate and control their own motion behaviour in real time according the state of the target during the target tracking task.This study mainly focuses on the problem of real-time tracking of single or multiple targets by multi-UAVs system,and maintaining dynamic network connectivity and collision avoidance at all times during the tracking process.The main work is shown as follows:（1）Aiming at the problem of single target tracking,this study proposes a robust and real-time switched multi-UAVs cooperative target tracking strategy（STS）.Aiming at the deficiency of existing algorithms in maintaining network topology connectivity and target acquisition success rate,a switching motion mode is proposed.Considering the fact that the target motion trajectory is not single,the target motion model is established,and the optimal estimation of the target state is obtained according to the Kalman consensus filter algorithm.Then,according to the change of the distance between the UAV and the target,different motion equations are set.Finally,Lyapunov method is used for theoretical analysis.The simulation results show that this strategy has high efficiency,and the success rate of the UAV to complete the tracking task is more than 95%.（2）Aiming at the problem of multitarget tracking,this study proposes a multitarget tracking control algorithm under local information selection interaction mechanism（A-LISI）.First,on the basis of the location,number and perceived target information of neighboring UAVs,a temporary leader selection strategy is designed to realize the local follow-up movement of UAVs when the UAV cannot fully perceive the target.Second,in combination with the basic rules of cluster movement and target information perception factors,distributed control equations are designed to achieve a rapid gathering of UAVs and consistent tracking of multiple targets.Lastly,simulation experiments are conducted in two and three-dimensional spaces,proved that the algorithm can complete the task of tracking multiple targets at the same time,and the algorithm is less time consumption for grouping,and the equal probability of the UAV subgroup size after group separation is over 78%.