Dynamic Task Allocation Method for UAV Air‐to‐Ground Strike Based on Double‐Layer Situation Assessment

To address the challenges of battlefield search and rescue (SAR) scenarios, the paper proposes a task allocation method for unmanned aerial vehicle (UAV) air‐to‐ground strike. The method decomposes the key process of task allocation into three phases: comprehensive situation assessment, individual situation assessment, and task allocation calculation. The paper employs the Bayesian network (BN) to assess and rank enemy targets, and generates a Comprehensive Situation Value (CSV) as the priority of task allocations. Then, the paper combines the entropy weight method and the Analytic Hierarchy Process (AHP) to quantify the priority of individual UAV attacks. The method yields an Individual Situation Value (ISV) for individual situation assessment. These assessment results are integrated into the Nondominated Sorting Genetic Algorithm II (NSGA‐II), incorporating multiobjective weight factors to optimize task allocation dynamically. Simulation experiments demonstrate that the proposed method significantly enhances mission success rates and operational effectiveness while reducing UAV attrition and mission completion time by balancing total mission priorities, individual attack priority, and total firepower allocation. The method′s adaptability to dynamic battlefield conditions such as UAV losses further validates its practicality and efficiency for multi‐UAV missions.