贝叶斯网络的对地攻击无人机自主能力评估

作战任务条件下评估对地攻击无人机自主能力，是无人机作战使用亟待解决的关键问题之一。针对对地攻击无人机作战流程与特点，选取感知探测、规划决策、作战执行、安全管理和学习进化5个紧密贴合其作战运用特点的自主能力影响因素，构建面向全任务过程的对地攻击无人机自主能力评估指标体系。基于贝叶斯网络建立自主能力评估模型，利用改进熵权法确定根节点的先验概率，并运用贝叶斯软件Netica进行实例仿真。针对对地攻击无人机任务前、任务中和任务后的自主能力，利用因果推理、截断分析推理和影响因素推理3种推理模式进行仿真验证及推理分析，根据仿真验证结果给出了各个阶段自主能力的动态调整建议。

Autonomous Capability Evaluation of GroundC-Attack UAV Based on Bayesian Network

Evaluating the autonomous capability of groundC-attack unmanned aerial vehicle (UAV) under the condition of combat mission is one of the key problems to be solved urgently in the combat use of UAV. According to the operational process and characteristics of groundC-attack UAV, five autonomous capability influencing factors closely fitting its operational characteristics are selected, including perceptual detection, planning and decisionC-making, operational execution, security management and learning evolution, to build an evaluation index system of autonomous capability of groundC-attack UAV for the whole mission process. An autonomous capability evaluation model is established based on Bayesian network, the prior probability of root node is determined by improved entropy weight method, and an example is simulated by Bayesian software Netica. Aiming at the autonomous capability of groundC-attack UAV before, during and after mission, three reasoning modes of causal reasoning, truncation analysis reasoning and influencing factor reasoning are used for simulation verification and reasoning analysis. According to the simulation verification results, the dynamic adjustment suggestions of autonomous capability in each stage are given.