Kalman Filter with Adaptive Covariance Estimation for Carrier Tracking under Weak Signals and Dynamic Conditions

Kalman filtering (KF)-based tracking has been commonly employed in global navigation satellite system (GNSS) receivers to achieve robust tracking. However, under more serious conditions, such as severe strength attenuation and abrupt dynamic coexisting environments, it is difficult for KF-based tracking to keep tracking well due to the fixed noise statistics. To further enhance the carrier tracking performance, this paper proposes an adaptive KF carrier tracking method for resisting signal strength fading and high dynamic environments. The proposed method introduces the adaptive factor to adjust the process noise covariance to accommodate the noise statistics in actual variable situations. Moreover, we apply the chi-square hypothesis test to detect system stability. The adaptive factor is only applied when the system is not stable, which can enhance computational efficiency. The proposed method is conducted in the GPS L1 software receivers. According to the results, the proposed algorithm can improve the robustness in tracking performance compared with other tracking methods under signal serious fading and high dynamic conditions. Using the proposed method, GNSS receivers' navigation performance can be improved under complex conditions.