Robust spoofing detection and mitigation in GNSS using iterative refinement and adaptive filtering

Global Navigation Satellite Systems (GNSSs) face significant security threats from spoofing attacks. Typical anti-spoofing methods rely on estimating the delays between spoofing and authentic signals using multicorrelator outputs. However, the accuracy of the delay estimation is limited by the spacing of the correlators. To address this, an innovative anti-spoofing method is introduced, which incorporates distinct coarse and refined stages for more accurate spoofing estimation. By leveraging the coarse delay estimates obtained through maximum likelihood estimation, the proposed method establishes the Windowed Sum of the Relative Delay (WSRD) statistics to detect the presence of spoofing signals. The iterative strategy is then employed to enhance the precision of the delay estimation. To further adapt to variations in the observation noise caused by spoofing intrusions and restore precise position, velocity, and timing solutions, an adaptive extended Kalman filter is proposed. This comprehensive framework offers detection, mitigation, and recovery against spoofing attacks. Experimental validation using datasets from the Texas Spoofing Test Battery (TEXBAT) demonstrates the effectiveness of the proposed anti-spoofing method. With 41 correlators, the method achieves a detection rate exceeding 90% at a false alarm rate of 10⁻⁵, with position or time errors below 15 m. Notably, this refined anti-spoofing approach shows robust detection and mitigation capabilities, requiring only a single antenna without the need for additional external sensors. These advancements can significantly contribute to the development of GNSS anti-spoofing measures.