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Low Complexity Carrier-to-Noise Ratio Estimators for GNSS Digital Receivers

78

Citations

22

References

2011

Year

Abstract

In a GNSS receiver the measure of C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> is not only supplementary information provided to the user together with the PVT (position, velocity, time), but it is also fundamental information to determine the status of the tracking subsystems and to control the receiver in critical environments. A classic approach to estimate the C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> in a navigation receiver is known as the narrowband-wideband power ratio method (NWPR). We investigate the suitability of different estimation methods, properly adapted from the digital communications world where a wide range of literature is available on the problem of estimating the signal-to-noise power ratio (SNR) of M-PSK modulations in additive white Gaussian noise (AWGN). The objective here is to identify a subset of candidate algorithms for SNR estimation, previously proposed for communications receivers. These methods are compared in specific situations of interest, such as weak signals or high signal-to-noise conditions obtained with high-gain antennas, and considering software defined radio receiver implementations. Simulation campaigns and a set of real GPS-L1 signals are used to test and compare the performance of the selected algorithms in a broad range of C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> values (from 20 to 70 dBHz), where the classic NWPR method is used as an authoritative benchmark. We conclude that a number of low-complexity algorithms for SNR estimation, based on a simple communication signal model, can be tailored to digital GNSS receivers. The performance is comparable with respect to the NWPR approach, but the complexity is reduced and possible improvements can be foreseen using the tested algorithms.

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