In a conventional GPS receiver, the carrier tracking system is the key stage that keeps the receiver locked to the radio navigation parameters (RNPs) of the received signal. The most commonly used approaches to the tracking system are phase lock loop (PLL), frequency lock loop (FLL), and FLL-assisted PLL. The main limitation of the above approaches is that their performance deteriorates when working with weak signals and in harsh environments. In recent years, Kalman filter (KF)-based tracking loop architectures have gained increased attention due to their robust and better performance compared with conventional architectures. In this paper, a novel Gauss–Hermite Kalman filtering-based carrier tracking algorithm is proposed for static and moving receivers with weak GPS signals. The performance of the proposed algorithm is compared with two other approaches: extended Kalman filter (EKF) and unscented Kalman filter (UKF). Simulations were conducted using a software-defined GPS simulator and software device radio (SDR) modules. A comparative analysis of the tracking methods demonstrated that the proposed tracking method shows a better performance and improves the tracking sensitivity and capability under weak signal conditions as compared with EKF- and UKF-based tracking methods. In addition, the results show that the proposed approach improves the Doppler frequency measurement accuracy under dynamic operation conditions.

A new adaptive unscented Kalman filter (AUKF) is proposed to estimate the radio navigation parameters of a GPS signal tracking system in noisy environments and on a highly dynamic object. The experimental results have shown that the proposed AUKF-based method improves the GPS tracking margin by approximately 8 and 3 dB as compared to the conventional algorithm and the KF-based tracking, respectively. At the same time, the accuracy of Doppler frequency measurements increases as well.