Multi-target assignment hunting strategy of UAV swarm based on improved K-means algorithm and shortest time mechanism

Multi-target hunting of UAV swarm is an important tactical means. This paper proposes a hunting strategy based on improved K-means and the shortest time mechanism. The large-scale task assignment problem is complex in structure and difficult to solve. To obtain higher hunting efficiency and reduce the amount of calculation on the single UAV, the hybrid architecture is used to decompose the complex multi-target hunting problem into a set of tasks that the UAV need to perform, which reduces the coupling of the system and the complexity of problem. Firstly, the multi-target hunting problem is stratified by the improved K-means algorithm to form multiple independent single target hunting subsystems. In the subsystem, the single target hunting task is decomposed into multiple subtasks that are easy to be executed by UAVs, and a one-to-one matching relationship between subtasks and UAVs is established by using the shortest time mechanism. UAV swarm can achieve multi-target hunting only by executing subtasks. The simulation results show that the UAV swarm can effectively allocate the multi-target hunting problem, which proves the effectiveness of the allocation strategy is proved.