Application of variational autoencoder combined with clustering algorithm in air combat situation assessment

Aimed at the problems of traditional assessment methods, such as the subjectivity of determining the weights, the weak ability of processing big data and the lack of feature extraction ability, an improved air combat situation assessment method based on VAE (variational autoencoder) and clustering algorithm was proposed. Firstly, according to the characteristic of continuity of situation changes, a situation classification method based on time period data was proposed, and the situation of both sides was divided into four categories. Then, on the basis of VAE, a VAE-WRBM-MDN feature extraction model was proposed, which used the MDN (mixed density network) to optimize VAE feature extraction capability as well as the similarity of generated data, and to optimize initial weights of the network with WRBM (weighted uncertainty restricted Boltzmann machines). Finally, the extracted features were input into two typical clustering algorithms for clustering, and then the situational function and actual battlefield conditions were used to modify the clustering results, so as to forming a correct situation classification criteria. In the process of experiments, the optimal parameters adjustment, key feature extraction, clustering and correction were performed. Experimental results show that the model classification accuracy rate and the model runtime both meet the application requirements. In addition, the assessment results of the example are consistent with the actual situation. Therefore, the proposed method is of practical value. © 2019, NUDT Press. All right reserved.