Low-cost COTS GNSS interference detection and classification platform: Initial results - Van Der Merwe, Johannes Rossouw; Contreras, David Franco; Jdidi, Dorsaf; Feigl, Tobias; Ruegamer, Alexander; Felber, Wolfgang (Proc. Intl. Conf. on Localization and GNSS (ICL-GNSS), 2022)
Interference signals cause position errors and outages to GNSS receivers. However, to solve these problems, the interference needs to be detected, classified, its purpose determined, and localized, such that it can be eliminated. Several interference monitoring solutions exist, but these are expensive, resulting in fewer nodes that may miss spatially sparse interference signals. This paper introduces a low-cost COTS GNSS interference monitoring, detection, and classification receiver. It employs ML on tailored signal pre-possessing of the raw signal samples and GNSS measurements to facilitate a generalized, high-performance architecture that does not require calibration. Therefore, the low-cost receivers with high performance can justify significantly more receivers to be deployed, resulting in a significantly higher POI. The initial results of controlled interference scenarios demonstrate detection and classification capabilities exceeding conventional approache
Keywords: Global navigation satellite system (GNSS); interference; detection; classification; machine learning (ML); commercial-off-the-shelf (COTS