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Article

Low Complexity Robust Data Demodulation for GNSS

1
Telecommunications for Space and Aeronautics Lab (TéSA), 31500 Toulouse, France
2
INP-ENSEEIHT, University of Toulouse, 31000 Toulouse, France
3
CNES, 31400 Toulouse, France
4
Thales Alenia Space, 31100 Toulouse, France
5
Electrical and Computer Engineering Department, Northeastern University, Boston, MA 02115, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Daniele Borio
Sensors 2021, 21(4), 1341; https://doi.org/10.3390/s21041341
Received: 21 December 2020 / Revised: 6 February 2021 / Accepted: 9 February 2021 / Published: 13 February 2021
(This article belongs to the Special Issue Robust Processing for GNSS)
In this article, we provide closed-form approximations of log-likelihood ratio (LLR) values for direct sequence spread spectrum (DS-SS) systems over three particular scenarios, which are commonly found in the Global Navigation Satellite System (GNSS) environment. Those scenarios are the open sky with smooth variation of the signal-to-noise ratio (SNR), the additive Gaussian interference, and pulsed jamming. In most of the current communications systems, block-wise estimators are considered. However, for some applications such as GNSSs, symbol-wise estimators are available due to the low data rate. Usually, the noise variance is considered either perfectly known or available through symbol-wise estimators, leading to possible mismatched demodulation, which could induce errors in the decoding process. In this contribution, we first derive two closed-form expressions for LLRs in additive white Gaussian and Laplacian noise channels, under noise uncertainty, based on conjugate priors. Then, assuming those cases where the statistical knowledge about the estimation error is characterized by a noise variance following an inverse log-normal distribution, we derive the corresponding closed-form LLR approximations. The relevance of the proposed expressions is investigated in the context of the GPS L1C signal where the clock and ephemeris data (CED) are encoded with low-density parity-check (LDPC) codes. Then, the CED is iteratively decoded based on the belief propagation (BP) algorithm. Simulation results show significant frame error rate (FER) improvement compared to classical approaches not accounting for such uncertainty. View Full-Text
Keywords: GNSS; robust LLR; low complexity; SNR mismatch; noise estimation; bayesian inference; interference countermeasure GNSS; robust LLR; low complexity; SNR mismatch; noise estimation; bayesian inference; interference countermeasure
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MDPI and ACS Style

Ortega, L.; Poulliat, C.; Boucheret, M.L.; Aubault Roudier, M.; Al-Bitar, H.; Closas, P. Low Complexity Robust Data Demodulation for GNSS. Sensors 2021, 21, 1341. https://doi.org/10.3390/s21041341

AMA Style

Ortega L, Poulliat C, Boucheret ML, Aubault Roudier M, Al-Bitar H, Closas P. Low Complexity Robust Data Demodulation for GNSS. Sensors. 2021; 21(4):1341. https://doi.org/10.3390/s21041341

Chicago/Turabian Style

Ortega, Lorenzo, Charly Poulliat, Marie L. Boucheret, Marion Aubault Roudier, Hanaa Al-Bitar, and Pau Closas. 2021. "Low Complexity Robust Data Demodulation for GNSS" Sensors 21, no. 4: 1341. https://doi.org/10.3390/s21041341

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