C/N0 Estimator Based on the Adaptive Strong Tracking Kalman Filter for GNSS Vector Receivers

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.

[1]  David M. Bevly,et al.  Comparison in the Performance of the Vector Delay/Frequency Lock Loop and Equivalent Scalar Tracking Loops in Dense Foliage and Urban Canyon , 2011 .

[2]  Li-Ta Hsu,et al.  Open-source MATLAB code for GPS vector tracking on a software-defined receiver , 2019, GPS Solutions.

[3]  Z. Hang A SUBOPTIMAL MULTIPLE FADING EXTENDED KALMAN FILTER , 1991 .

[4]  Mark Petovello What are vector tracking loops , and what are their benefits and drawbacks ? , 2022 .

[5]  John Y. Hung,et al.  Performance Analysis of Vector Tracking Algorithms for Weak GPS Signals in High Dynamics , 2009, IEEE Journal of Selected Topics in Signal Processing.

[6]  Emanuela Falletti,et al.  Low Complexity Carrier-to-Noise Ratio Estimators for GNSS Digital Receivers , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Matthew Lashley,et al.  Modeling and Performance Analysis of GPS Vector Tracking Algorithms , 2009 .

[8]  Daniele Borio,et al.  C/N 0 estimation: design criteria and reliability analysis under global navigation satellite system (GNSS) weak signal scenarios , 2012 .

[9]  Paul D. Groves,et al.  GPS signal to noise measurement in weak signal and high interference environments , 2005 .

[10]  Shaojun Feng,et al.  Design of an adaptive GPS vector tracking loop with the detection and isolation of contaminated channels , 2017, GPS Solutions.

[11]  Gonzalo Seco-Granados,et al.  C/N0 estimators for high-sensitivity snapshot GNSS receivers , 2018, GPS Solutions.

[12]  Daniele Borio,et al.  C/N0 Estimation for Modernized GNSS Signals: Theoretical Bounds and a Novel Iterative Estimator , 2010 .

[13]  Han Gao Environmental Context Detection for Adaptive Navigation using GNSS Measurements from a Smartphone , 2018 .

[14]  Dennis M. Akos,et al.  An Open Source GPS/GNSS Vector Tracking Loop - Implementation, Filter Tuning, and Results , 2011 .

[15]  D. Akos,et al.  GPS C/N/sub 0/ estimation in the presence of interference and limited quantization levels , 2007, IEEE Transactions on Aerospace and Electronic Systems.