A Robust Tracking Loop Using Adaptive H_∞ Unscented Kalman Filter in GNSS Receivers

In the context of complex dynamic environments, a robust tracking loop is essential for achieving accurate positioning with GNSS receivers. The tracking loop based on the Kalman filter significantly improves the tracking accuracy and dynamic performance. However, Kalman filtering relies on accurate prior knowledge to achieve optimal estimation. In practical applications, satellite signals are subject to various uncertainties, which can severely degrade tracking performance. To enhance tracking robustness, this paper proposes an adaptive $\boldsymbol{H}_{\infty}$ unscented Kalman filter (AHUKF) based on robust control theory. By utilizing the correlator output as measurement information, a tracking loop is devised based on AHUKF to effectively mitigate the impact of unknown signal noise statistics in complex scenarios. Field vehicle experiment demonstrates that the proposed method effectively suppresses the impact of external interference on the tracking loop, thereby indirectly enhancing receiver accuracy.