融合信息图的优化哈里斯鹰多无人机动态目标搜索

In the study of cooperative search for moving targets by multi-Unmanned Aerial Vehicle(multi-UAV),this paper proposes a cooperative search decision-making method that integrates an information graph model with the optimized Harris hawk optimization algorithm. A target probability information graph model based on Gaussian distribution is constructed,which enhances the certainty of target existence probability in the environment through the establishment of a certainty information graph model. Furthermore,a digital information pheromone graph with attraction and repulsion mechanisms is used to guide UAVs to move towards unexplored areas,effectively reducing repetitive search behaviors and enhancing the efficiency of cooperative search. To address the issue of Harris hawk optimization algorithm being prone to local optima,a non-linear energy factor updating strategy is proposed,integrating the optimal individual positions and presenting a new position update formula. Finally,to search for the targets with randomly changing trajectories,a revisitable digital information graph and an adaptive target search gain function are designed to enhance the capability of UAVs to capture moving targets. Simulation results verify the effectiveness of the improved Harris hawk optimization algorithm in cooperative search for moving targets by multiple UAVs.