Air combat situation assessment based on differential window generative adversarial network
Aiming at the complex composition and missing tags of the flight reference data collected by the aircraft during the air combat,a semi-supervised air combat situation assessment model is proposed based on the generative adversarial network(GAN).Firstly,the main influencing factors of the air combat data are extracted according to the weights of each element,then the differencing and windowing processing are carried out.The situation information is relativeized into a one-dimensional feature vector using the differential method.The windowing information generates a feature matrix reflecting the situation information of the two carrier aircrafts,which is sent to the network for semi-supervised training.Simulation results show that the model has a good situation analysis effect in the case of sample labels missing,and the recognition accuracy of the four situations is 90.91％.
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