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

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.