基于优化随机森林的对地攻击无人机自主作战效能评估

Assessment of Autonomous Combat Effectiveness of Ground-Attack UAV Based on Optimized Random Forest

为实现高效、快速、客观地对对地攻击无人机自主作战效能进行评估,文中引入向量加权平均算法(Weighed Mean of Vectors Algorithm,INFO)和K折交叉验证方法对随机森林算法(Random Forest,RF)进行优化寻找最优参数组合,提出了基于优化随机森林的对地攻击无人机自主作战效能评估方法。首先,基于向量加权平均优化算法理论,对随机森林决策树模型数量以及最大深度两项超参数进行寻优。其次,结合对地攻击无人机作战任务,对自主作战效能评估的主要作战因素进行分析,归纳总结了对地攻击无人机自主作战效能评估指标体系,并建立了基于INFO-RF的无人机自主作战效能评估模型。最后,通过对评估模型进行实例验证并与其他方法进行对比分析,结果表明,相较于传统RF模型、GA-RF模型和SVM模型,INFO-RF模型输出结果具有较高的拟合度和更为精确的评估值,实例结果有效验证了所提方法的合理性和优化模型的可靠性。

In order to evaluate the autonomous combat effectiveness of ground-attack UAV efficiently,quickly and objectively,weighted mean of vectors algorithm(INFO)and K-fold cross-validation method are introduced to optimize the random forest algorithm(RF)to find the optimal parameter combination,and an autonomous combat effectiveness evaluation method based on optimized random forest is proposed.Firstly,based on the theory of weighted mean of vectors algorithm,the number and maximum depth of the random forest decision tree model are optimized.Secondly,combined with the ground-attack UAV combat tasks,the main operational factors of autonomous combat effectiveness evalua-tion are analyzed,the evaluation index system of autonomous combat effectiveness of ground-attack UAV is summarized,and the evaluation model of autonomous combat effectiveness of UAV based on INFO-RF is established.Finally,the evaluation model is verified by an example and compared with the traditional RF model,GA-RF model and SVM model.The results show that the output results of INFO-RF model have higher fitting degree and more accurate evaluation value.The results of the example effectively verify the rationality of the proposed method and the reliability of the optimization model.