|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.|
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.
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