P3. Detection, classification, identification and mitigation of GNSS signal degradations by means of machine learning

Among the navigation means, Global Navigation Satellites Systems (GNSS), and namely the Global Positioning System (GPS), have become essential and the availability of a GNSS navigation solution on board seems completely natural. However, the quality of the position calculated by the on-board equipment may be reduced when the received signal is degraded. This degradation can find its origin in a defect of the signal generation system, carried by the satellite, or in the receiving conditions, typically when interferences or multipaths are in addition to the desired signal.
The objectives of the thesis were to detect, classify, identify and finally reduce the impairments of the GNSS signals seen by the on-board receiver, by means of Machine Learning techniques.

More specifically, the performance of Machine Learning methods has been assessed on the signal at the correlator output, the correlator output in short. Indeed, the correlator output is a key element in the calculation of the aircraft’s position by the receiver, and, consequently, it is the link in the signal processing chain where the degradations have the most significant impact.

Correlations of the received signal with a local replica over a (Doppler shift, propagation delay)-grid are mapped into grayscale 2D images. They depict the received information possibly contaminated by multipath propagation. The images feed a Convolutional Neural Network (CNN) for automatic feature construction and multipath pattern detection.

The issue of unavailability of a large amount of supervised data required for CNN training has been overcome by the development of a synthetic data generator. It implements a well-established and documented theoretical model. A comparison of synthetic data with real samples is proposed.
The complete framework is tested for various signal characteristics and algorithm parameters. The prediction accuracy does not fall below 93% for Carrier-to-Noise ratio (C/N0) as low as 36 dBHz, corresponding to poor receiving conditions. In addition, the model turns out to be robust to the reduction of image resolution.