4D trajectory lightweight prediction algorithm based on knowledge distillation technique

To address the challenges of current 4D trajectory prediction-specifically, limited multi-factor feature extraction and excessive computational cost-this study develops a lightweight prediction framework tailored for real-time air-traffic management.

We propose a hybrid RCBAM-TCN-LSTM architecture enhanced with a teacher-student knowledge distillation mechanism. The Residual Convolutional Block Attention Module (RCBAM) serves as the teacher network to extract high-dimensional spatial features via residual structures and channel-spatial attention. The student network adopts a Temporal Convolutional Network-LSTM (TCN-LSTM) design, integrating dilated causal convolutions and two LSTM layers for efficient temporal modeling. Historical ADS-B trajectory data from Zhuhai Jinwan Airport are preprocessed using cubic spline interpolation and a uniform-step sliding window to ensure data alignment and temporal consistency. In the distillation process, soft labels from the teacher and hard labels from actual observations jointly guide student training.

In multi-step prediction experiments, the distilled RCBAM-TCN-LSTM model achieved average reductions of 40%-60% in MAE, RMSE, and MAPE compared with the original RCBAM and TCN-LSTM models, while improving R ^² by 4%-6%. The approach maintained high accuracy across different prediction horizons while reducing computational complexity.

The proposed method effectively balances high-precision modeling of spatiotemporal dependencies with lightweight deployment requirements, enabling real-time air-traffic monitoring and early warning on standard CPUs and embedded devices. This framework offers a scalable solution for enhancing the operational safety and efficiency of modern air-traffic control systems.