A Carrier Tracking Loop Using Adaptive Strong Tracking Kalman Filter in GNSS Receivers

The Kalman filter (KF) has been widely used in the carrier track to improve the tracking performance of receivers under challenging environments. The KF-based tracking assumes that the noise statistics are exactly known in advance and kept fixed during the whole iteration process. However, the noise statistics are difficult to know accurately and the fixed noise statistics cannot reflect the practical situations under time-varying environments. To further enhance the performance of carrier tracking, the adaptive strong tracking Kalman filter (STKF) is proposed. The adaptive fading factors are employed in the state prediction covariance to adjust the Kalman gain. Moreover, to improve the accuracy of fading factors in STKF tracking, the measurement noise covariance is adjusted based on the $C/N_{0}$ estimations. In addition, the working state is checked, and fading factors are used only when the system is not steady. The proposed algorithm has been implemented in the software receiver. The test results demonstrate that the proposed method has more superior tracking performance under challenging environments than other tracking methods.

I. Introduction

For the global navigation satellite system (GNSS) receivers, the carrier tracking loop is used to synchronize the local carrier with the incoming signals, which is a significantly important part. The phase lock loop (PLL) is commonly used in the carrier tracking loop, but it is extremely fragile especially under challenging environments. To enhance tracking performance under harsh environments, the Kalman filter (KF) has been applied in the tracking loop to achieve robust tracking [1]. Several studies have shown that the KF-based tracking outperforms the PLL tracking under harsh conditions [1], [2].