Neural Network based Evil WaveForms Detection

Alexis Louis and Mathieu Raimondi

Abstract:	Distortions of GNSS signals can lead to unacceptable pseudo-range errors. The object of study is a certain type of distortion — evil waveforms (EWF) — which is a rare perturbation occuring at the stage of signal generation. Detecting those distortions post-correlation traditionally involve designing hand crafted structure tests on a densely sampled autocorrelation function (ACF). However, traditional hand crafted tests have to be designed for specific scenarios hence lack flexibility compared to data-based methods. A neural network architecture capable of processing the structure of the ACF is proposed, implicitly learning structure tests, in order to tackle the evil waveforms detection problem.
Published in:	Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020) September 21 - 25, 2020
Pages:	1984 - 1989
Cite this article:	Louis, Alexis, Raimondi, Mathieu, "Neural Network based Evil WaveForms Detection," Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020), September 2020, pp. 1984-1989. https://doi.org/10.33012/2020.17651
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