A distributed time-varying neurodynamic algorithm for multi-UAV collaborative target tracking problem in maritime search and rescue.

This work investigates the problem of collaborative target tracking by multiple unmanned aerial vehicles (UAVs) in maritime search and rescue. A class of time-varying (TV) convex optimization problems with inequality constraints is presented. In contrast to existing studies that address UAV-based maritime search and rescue under fixed wind speed conditions, this study also explores collaborative target tracking by UAVs under varying wind speed conditions. A distributed TV neurodynamic algorithm is designed using the prediction-correction method and sliding mode control technique. By constructing suitable Lyapunov functions, the proposed algorithm, whose fixed-time convergence property is theoretically proven, exhibits a convergence time independent of the initial state. In the context of multi-UAV collaborative target tracking experiments, the target's three-dimensional trajectory equations were established for three distinct cases, each incorporating sine and cosine functions along the x and y axes. The experiments demonstrate that the tracking efficiency of the multi-UAV system is unaffected by the TV target trajectory.