Design and Simulation of Multi-Target Strategy Based on Multi-Dimensional UAV Series Detection

This paper proposes an innovative tandem multi-target search coordination control strategy to solve the challenge of multi-UAV searching multi-targets simultaneously in a complex unknown environment. First, we combine the closed-loop adjustment and cooperation mechanism to develop an enhanced response threshold multi-objective task assignment model based on the multi-objective assignment model of the target response threshold, which effectively solves the problem of uneven distribution of UAVs and enhances the efficiency of series detection. Secondly, we apply the two-dimensional extended Particle swarm optimization to the multi-dimensional UAV system and combine it with an improved adaptive inertia weight method to form a multi-dimensional extended particle swarm optimization algorithm with improved adaptive inertia weight. This effectively prevents the particles from falling into the local optimum and enhances the search efficiency. Finally, we integrate these two improved techniques into the UAV's series search process. Compared with the traditional extended particle swarm optimization algorithm, this method significantly reduces the system energy consumption and search time and is verified by numerical simulation.