Abstract:

Accurate prediction results of the target aircraft trajectory can greatly improve the ability of our aircraft to gain air superiority in the process of air combat. This paper proposes a Transformer-based Prediction Error Compensation （TFPEC） method to further improve the accuracy of the trajectory prediction results based on the benchmarks generated by mature trajectory prediction algorithms. Firstly， a Transformer-based feature extraction model is established to process the historical trajectory sequence of the target aircraft， so as to obtain the trajectory feature matrix. Secondly， the multi-layer perceptron structure is used to perform regression calculation of the feature matrix， so as to calculate the trajectory prediction error compensation value. Thirdly， the Fourier transform is used to transform the trajectory into the frequency domain for modulo analysis， and to calculate the error compensation coefficient. Finally， the benchmark， the error compensation value， and the compensation coefficient are comprehensively calculated to obtain the final prediction result. The proposed method is tested on the one-on-one data set generated by pilots. The simulation results show that compared with the four existing trajectory prediction algorithms， the proposed method can effectively improve the accuracy of trajectory prediction results， and is applicable for aircraft trajectory prediction tasks in the air combat environment.

Key words: air combat trajectory prediction, sequence feature extraction, error compensation, Transformer, Fourier transform