Computational Intelligence Based Multi-UAV Target Assignment and Tracking Algorithm

This paper addresses the challenge of multiple unmanned aerial vehicles target assignment and dynamic tracking within communication-constrained environments. We propose a framework based on computational intelligence (CI), which integrates two key components. First, for target assignment, we introduce a distributed Hungarian algorithm (DHA). The DHA leverages local information exchange and iterative optimization to achieve efficient assignment, thereby mitigating the reliance on global information while preserving the optimality characteristics of the traditional Hungarian algorithm. Second, for target tracking, we employ a two-stage deep reinforcement learning (DRL) algorithm based pre-trained clone learning. This approach enables the UAV team to learn adaptive strategies for achieving sustained cooperative tracking. Simulation results demonstrate that the proposed framework yields both efficient assignment and accurate tracking performance in dynamic environments.