Currently, cybersecurity and cyber resilience are emerging and urgent issues in nextgeneration air traffic surveillance systems, which depend primarily on Automatic Dependent Surveillance-Broadcast(ADS-B) owing to its...Currently, cybersecurity and cyber resilience are emerging and urgent issues in nextgeneration air traffic surveillance systems, which depend primarily on Automatic Dependent Surveillance-Broadcast(ADS-B) owing to its low cost and high accuracy. Unfortunately, ADS-B is prone to cyber-attacks. To verify the ADS-B positioning data of aircraft, multilateration(MLAT)techniques that use Time Differences of Arrivals(TDoAs) have been proposed. MLAT exhibits low accuracy in determining aircraft positions. Recently, a novel technique using a theoretically calculated TDoA fingerprint map has been proposed. This technique is less dependent on the geometry of sensor deployment and achieves better accuracy than MLAT. However, the accuracy of the existing technique is not sufficiently precise for determining aircraft positions and requires a long computation time. In contrast, this paper presents a reliable surveillance framework using an Actual TDoA-Based Augmentation System(ATBAS). It uses historically recorded real-data from the OpenSky network to train our TDoA fingerprint grid network. Our results show that the accuracy of the proposed ATBAS framework in determining the aircraft positions is significantly better than those of the MLAT and expected TDoA techniques by 56.93% and 48.86%, respectively. Additionally, the proposed framework reduced the computation time by 77% compared with the expected TDoA technique.展开更多
文摘Currently, cybersecurity and cyber resilience are emerging and urgent issues in nextgeneration air traffic surveillance systems, which depend primarily on Automatic Dependent Surveillance-Broadcast(ADS-B) owing to its low cost and high accuracy. Unfortunately, ADS-B is prone to cyber-attacks. To verify the ADS-B positioning data of aircraft, multilateration(MLAT)techniques that use Time Differences of Arrivals(TDoAs) have been proposed. MLAT exhibits low accuracy in determining aircraft positions. Recently, a novel technique using a theoretically calculated TDoA fingerprint map has been proposed. This technique is less dependent on the geometry of sensor deployment and achieves better accuracy than MLAT. However, the accuracy of the existing technique is not sufficiently precise for determining aircraft positions and requires a long computation time. In contrast, this paper presents a reliable surveillance framework using an Actual TDoA-Based Augmentation System(ATBAS). It uses historically recorded real-data from the OpenSky network to train our TDoA fingerprint grid network. Our results show that the accuracy of the proposed ATBAS framework in determining the aircraft positions is significantly better than those of the MLAT and expected TDoA techniques by 56.93% and 48.86%, respectively. Additionally, the proposed framework reduced the computation time by 77% compared with the expected TDoA technique.
