Abstract
The carrier-to-noise ratio (C/N0) is an important indicator of the signal quality of global navigation satellite system receivers. In a vector receiver, estimating C/N0 using a signal amplitude Kalman filter is a typical method. However, the classical Kalman filter (CKF) has a significant estimation delay if the signal power levels change suddenly. In a weak signal environment, it is difficult to estimate the measurement noise for CKF correctly. This article proposes the use of the adaptive strong tracking Kalman filter (ASTKF) to estimate C/N0. The estimator was evaluated via simulation experiments and a static field test. The results demonstrate that the ASTKF C/N0 estimator can track abrupt variations in C/N0 and the method can estimate the weak signal C/N0 correctly. When C/N0 jumps, the ASTKF estimation method shows a significant advantage over the adaptive Kalman filter (AKF) method in terms of the time delay. Compared with the popular C/N0 algorithms, the narrow-to-wideband power ratio (NWPR) method, and the variance summing method (VSM), the ASTKF C/N0 estimator can adopt a shorter averaging time, which reduces the hysteresis of the estimation results.
Highlights
Global navigation satellite system (GNSS) receivers use tracking loops to synchronize replicated signals with received signals to maintain a continuous lock on the received signals
Compared with the popular C/N0 algorithms, the narrow-to-wideband power ratio (NWPR) method, and the variance summing method (VSM), the adaptive strong tracking Kalman filter (ASTKF) C/N0 estimator can adopt a shorter averaging time, which reduces the hysteresis of the estimation results
Satellite 24 is selected for evaluation of the estimation performance of the ASTKF method when the signal condition in the environment is poor
Summary
Global navigation satellite system (GNSS) receivers use tracking loops to synchronize replicated signals with received signals to maintain a continuous lock on the received signals. The delay in the C/N0 estimation process may cause erroneous measurements to be passed to the navigation Kalman filter (KF), which would severely degrade the navigation performance For these reasons, the conventional C/N0 estimation algorithms that are discussed above can impair the performance of vector-tracking loops in harsh environments. In a weak signal environment, the amplitude Kalman filter has difficulty accurately determining the measurement noise. Based on this estimation method, an adaptive C/N0 estimation method is presented in [6]. The C/N0 estimation algorithm adaptively switches between the differential method and the strong tracking Kalman filter (STKF) estimator. Based on the software receiver, vector-tracking loops and an adaptive C/N0 estimation algorithm are implemented. The conclusions of this study are presented in the final section
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