Coverage-based Cooperative Searching Method for UAV Swarm under Multi-sensor errors

In recent years, the threat of UAV swarm weapons to air security is increasing, the full coverage detection of the incoming target swarm and the acquisition of the global movement information of the target is the premise and key to achieve efficient interception. Therefore, this paper proposes a coverage-based cooperative searching method for UAV swarm under multi-sensor errors. First, according to the typical spatial distribution characteristics of the incoming target swarm, the studied problem is simplified to a two-dimensional plane. Then, the detection probability distribution function of the ground-based rader and UAV airborne seeker are established, respectively. Based on these functions, a cooperative searching assignment model for maximizing detection probability of target swarm is proposed, where the flight safety distance constraint of the prusuer UAVs is also considered. Subsequently, the grey Wolf algorithm is applied to solve the multi-constraint multi-decision variable optimization probelm and generate the expected formation and line of sight direction of the UAV. Numerical simulation demonstrates that the proposed algorithm can efficiently detect the target swarm with a field coverage probability of no less than 99%, and the allocation time does not exceed 1.2 seconds.