False signal identification of ADS-B assisted by UAV cooperative localization

Automatic Dependent Surveillance-Broadcast (ADS-B) technology, with its open signal sharing, faces substantial security risks from false signals and spoofing attacks when broadcasting Unmanned Aerial Vehicle (UAV) information. This paper proposes a security position verification technique based on Multilateration (MLAT) to detect false signals, ensuring UAV safety and reliable airspace operations. First, the proposed method estimates the current position of the UAV by calculating the Time Difference of Arrival (TDOA), Time Sum of Arrival (TSOA), and Angle of Arrival (AOA) information. Then, this estimated position is compared with the ADS-B message to eliminate false UAV signals. Furthermore, a localization model based on TDOA/TSOA/AOA is established by utilizing reliable reference sources for base station time synchronization. Additionally, an improved Chan-Taylor algorithm is developed, incorporating the Constrained Weighted Least Squares (CWLS) method to initialize UAV position calculations. Finally, a false signal detection method is proposed to distinguish between true and false positioning targets. Numerical simulation results indicate that, at a positioning error threshold of 150 m, the improved Chan-Taylor algorithm based on TDOA/TSOA/AOA achieves 100% accuracy coverage, significantly enhancing localization precision. And the proposed false signal detection method achieves a detection accuracy rate of at least 90% within a 50-meter error range.