基于GRU-KAN的高速飞行器轨迹预测方法

High speed aircraft have the characteristics of fast flight speed, large maneuvering range, and strong breakthrough ability, which pose a significant threat to defense system. Accurately predicting the flight trajectory of enemy high-speed aircraft during the guidance phase can provide effective technical support for intercepting enemy missiles by mastering their flight path in advance. Therefore, this article proposes a trajectory prediction model based on Gated Recurrent Unit-Kolmogorov-Arnold Network (GRU - KAN) architecture for the guidance phase of high - speed aircraft. Firstly, establish a high - speed aircraft motion model in the ballistic coordinate system, and establish a trajectory database through a longitudinal jump maneuver model. Subsequently, the trajectory data is segmented and preprocessed using a sliding window to obtain the trajectory dataset. Finally, a trajectory prediction network is designed based on GRU and KAN architectures, with 50 s trajectory data as input and 150 s predicted trajectory data as output. The experimental results show that the model has a smaller network complexity, with maximum average prediction errors of 7.58 × l 0 ^{- 2} °, 9.48 × 10^-3°, and 7.51 × 10¹ m in the longitude, latitude, and altitude directions, respectively. The prediction errors in the longitude and latitude directions are not significantly different from those of traditional intelligent temporal prediction models, but in the altitude direction, the prediction results are 27.8% higher than traditional GRU prediction models and 70.5 % higher than LSTM prediction models.