Multi-UAV Collaborative Target Consensus Detection Using Enhanced Particle Swarm Optimization

Multi-UAV collaborative perception with region coverage has important applications in various fields such as post-disaster rescue, agricultural pest control, and enemy rear reconnaissance. The current region coverage algorithms focus on covering an area with the lowest redundancy and smallest coverage gap. However, simple swarm coverage methods cannot obtain accurate perception information for key target reconnaissance tasks. This paper focuses on the multi-UAV cross-coverage method to improve the accuracy of key target recognition. An improved particle swarm optimization algorithm is proposed under sensing and communication load constraints. Three improvements are implemented: particle velocity update strategy, weight update formula, and particle recombination strategy. The simulation results show the advantages of the proposed algorithm over classical methods and its effectiveness in cross-coverage and target consensus.