Ocean-Reflected GNSS Signals Detection with Generalized Likelihood Ratio Test

Santiago Ozafrain, Pedro A. Roncagliolo, Carlos H. Muravchik

Abstract:	Reflectometry with GNSS signals (GNSS-R) exploits opportunistically the signals of the satellite navigation systems that are reflected in the surface of the Earth to obtain geophysical information as a passive remote sensing technique. The reflected signals are very weak, so the sensors typically use large gain receiver antenna arrays and long periods of averaging to satisfactory detect them. Usually, the GNSS-R signals are processed into delay-Doppler maps, a representation of the power of the correlation of the received signal with a local GNSS signal replica for a range of code delay and Doppler shift values. This is the same procedure used for the direct signal acquisition, which can be seen as a composite hypothesis test that solves the detection problem with a model that fits that type of signal. In this work a new method for the GNSS-R signal acquisition is presented which takes advantage of a more representative model of the received reflected signal. The algorithm is found by solving a Generalized Likelihood Ratio Test and a theoretical performance analysis is presented that suggests a considerable gain in comparison with the traditional approach. This gain is also verified with empirical results by implementing and testing the new method with actual signals from the European Space Agency’s mission TechDemoSat-1, showing a great advantages of the proposed method over the classical approach.
Published in:	Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017) September 25 - 29, 2017 Oregon Convention Center Portland, Oregon
Pages:	3441 - 3452
Cite this article:	Ozafrain, Santiago, Roncagliolo, Pedro A., Muravchik, Carlos H., "Ocean-Reflected GNSS Signals Detection with Generalized Likelihood Ratio Test," Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017), Portland, Oregon, September 2017, pp. 3441-3452. https://doi.org/10.33012/2017.15348
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