A Machine Learning Evaluation of the Impact of Bit-Depth for the Detection and Classification of Wireless Interferences in Global Navigation Satellite Systems

The performance of the services provided by Global Navigation Satellite Systems (GNSSs) can be seriously degraded by the presence of wireless interferences, and Machine Learning (ML) has been applied to address this problem using the digital artifacts generated by the GNSS receiver. While such an application is not novel in the literature, the analysis of the impact of the bit-depth at which the GNSS signal is recorded has not received significant attention. The type and power level of the wireless interference are also important factors to investigate in this context. This paper addresses this gap by performing an extensive analysis of the impact of these factors on a data set of GNSS signals subject to three different types of wireless interferences with ML and DL algorithms. The analysis is a combination of a pre-processing phase where the Carrier-to-Noise Ratio (CNR) values of different satellites are evaluated, the extraction of relevant features for ML, and the application of a Convolutional Neural Network (CNN) with a multi-head attention layer. The results show that the proposed approach is able to detect the presence of interference with great accuracy (e.g., 99%) but the type of interference and bit-depth can decrease the performance.