A Coarse-to-Fine Adaptive Kalman Filter for Weak GNSS Signals Carrier Tracking

Abstract

The tracking performance of the standard Kalman filter (KF)-based carrier tracking depends heavily on the potentially insufficient priori noise covariance. The inadequate prior knowledge will degrade the tracking sensitivity when the receiver experiences weak signal conditions. To improve the tracking performance, a coarse-to-fine adaptive Kalman filter (AKF) carrier tracking approach is proposed. In this method, the process and measurement noise covariance are updated in real-time according to the measurement information. The AKF-based fine tracking is initialized by the coarse tracking results, which can improve the convergence speed of the AKF tracking. The proposed method has been conducted in the software receiver and compared with standard KF-based tracking and conventional tracking algorithms. The test results show that the proposed AKF-based tracking can improve tracking sensitivity under weak signal conditions. Moreover, the proposed method can reduce the convergence time compared with the AKF-based tracking without the coarse tracking stage.