Hypersonic glide vehicle trajectory prediction based on frequency enhanced channel attention and light sampling-oriented MLP network

• Published in: Defence technology 2024-11
• Main Authors: Cai, Yuepeng, Zhuang, Xuebin
• Format: Article
• Language: English
• Subjects: Frequency enhanced channel attention; Hypersonic glide vehicle; Light sampling-oriented MLP network; Trajectory prediction
• ISSN: 2214-9147; 2214-9147
• DOI: 10.1016/j.dt.2024.11.001

Hypersonic Glide Vehicles (HGVs) are advanced aircraft that can achieve extremely high speeds (generally over 5 Mach) and maneuverability within the Earth's atmosphere. HGV trajectory prediction is crucial for effective defense planning and interception strategies. In recent years, HGV trajectory prediction methods based on deep learning have the great potential to significantly enhance prediction accuracy and efficiency. However, it's still challenging to strike a balance between improving prediction performance and reducing computation costs of the deep learning trajectory prediction models. To solve this problem, we propose a new deep learning framework (FECA-LSMN) for efficient HGV trajectory prediction. The model first uses a Frequency Enhanced Channel Attention (FECA) module to facilitate the fusion of different HGV trajectory features, and then subsequently employs a Light Sampling-oriented Multi-Layer Perceptron Network (LSMN) based on simple MLP-based structures to extract long/short-term HGV trajectory features for accurate trajectory prediction. Also, we employ a new data normalization method called reversible instance normalization (RevIN) to enhance the prediction accuracy and training stability of the network. Compared to other popular trajectory prediction models based on LSTM, GRU and Transformer, our FECA-LSMN model achieves leading or comparable performance in terms of RMSE, MAE and MAPE metrics while demonstrating notably faster computation time. The ablation experiments show that the incorporation of the FECA module significantly improves the prediction performance of the network. The RevIN data normalization technique outperforms traditional min-max normalization as well.