An Interference Detection Method Based on CNN and CNR for GNSS

Global Navigation Satellite System (GNSS) signals are inherently weak and highly susceptible to jamming. Traditional signal analysis-based detection methods struggle with accurate identification in scenarios involving multipath propagation, weak interference, or complex interference types, leading in high missed detection rate (MDR). To solve these issues, a convolutional neural network (CNN) based interference detector by using carrier-to-noise density ratio (CNR) parameters from large-scale observation station is proposed in this paper. By learning the change principle in CNR features at lots of receiving stations under interference, a deep learning model is designed to effectively detect interference. In order to automatically extract the feature behind CNR data well, a CNN consisting of two convolutional layers, two max-pooling layers, and a fully connected dense block is built and trained. After preprocessing of normalization and dimensional transformation, the CNR observation data are fed into CNN to produce detection results. Simulation results demonstrate that a significant improvement in detection capabilities can be provided by the proposed method under both weak and complex interferences. Across the mixed dataset including 4 types of suppression jamming and 3 JSR values, an detection accuracy of 98.49% can be achieved by the CNN scheme, while only 95.12% is got by the baseline method, i.e RFA.