Indirect Kalman filtering for robust GNSS spoofing detection in signal quality monitoring

Abstract

Malicious spoofing poses a significant threat to the integrity of global navigation satellite system (GNSS), compromising the reliability and accuracy of measurements essential for navigation, positioning, and timing applications. Signal quality monitoring (SQM) metrics are crucial for detecting spoofing attacks by providing real-time assessments of GNSS signal integrity. However, noise interference in these metrics can degrade detection performance, leading to delayed alarms and reduced security. This paper introduces an innovative indirect Kalman filtering (IKF) approach to enhance spoofing detection by effectively mitigating the impact of noise on SQM metrics. The proposed method employs Kalman filtering to independently smooth metrics derived from the in-phase and quadrature channel outputs of multi-correlators. The non-coherent accumulation of the Kalman filter’s output state variables forms a comprehensive detection metric, while the steady-state noise variance of the Kalman filter’s output is utilized to compute the detection threshold. Theoretical analysis and experimental validation demonstrate that the IKF significantly outperforms traditional mean filtering methods in terms of detection accuracy and alarm latency. Experimental results indicate that the IKF achieves an average detection rate exceeding 80 % and reduces alarm times by approximately 20 s. This work provides a robust framework for real-time GNSS signal integrity monitoring and offers an effective solution for enhancing GNSS security against spoofing threats.