End-to-End Deep Learning for Flight Trajectory Reconstruction from Multi-Station ADS-B Measurements

In the field of aviation safety, ADS-B is widely adopted as an active broadcast-based aviation surveillance system, enabling aircraft to broadcast their real-time position information via onboard devices. However, the open communication protocol relied upon by ADS-B has led to increasingly frequent GPS spoofing and network hijacking attacks, posing significant threats to communication security. A reliable secondary verification method is urgently needed to revalidate the position information broadcast by aircraft. To address this issue, this paper proposes a deep learning framework that does not rely on specific message content but directly calculates aircraft trajectories through tamper-proof electromagnetic signals. Based on real flight trajectories and distributed sensor signals collected from the OpenSky dataset, we trained an End-to-End neural network, innovatively introducing heterogeneous sensor encoders and trajectory decoders, and demonstrated the effectiveness of the proposed model through empirical experiments. In comparison experiments, our proposed method achieved a breakthrough of 15.44% higher than the baseline in MDE and a single coordinate axis accuracy improvement of up to 22.77% in MRE. Finally, through ablation experiments and visualization analysis, we demonstrate the necessity of each component of the model and the overall effectiveness of trajectory reconstruction.