GNSS Jamming Attacks Recognition Based on Dual GCN With Adaptive Weight Learning

The baleful jamming attacks are destructive to the Global Navigation Satellite System (GNSS) receivers. Detecting the jamming type accurately at a GNSS receiver facilitates to select the corresponding mitigation method. Unfortunately, the recognition method based on the manual selection of the signal features relies on the independence of features. Considering the information of similar interference signals in the frequency domain is comparable, the existing deep learning methods, such as convolutional neural networks (CNNs) with local receptive fields, have difficulty in modeling and processing the global correlation between signals. To this end, a dual graph convolutional network (GCN) with adaptive weight learning (AWL-DGCN) method is proposed in this article. The interference signals are converted into graph data, and the GCN, which has demonstrated advantages in processing graph data, is employed for their analysis. More specifically, three kinds of time- and frequency-domain features are extracted to establish different homogeneous graphs between signals. Furthermore, the current graph aggregation method simply adds the homogeneous graphs that contain different information, making it difficult to dynamically balance the contributions of different information, which affects the effectiveness of message passing. Therefore, a dual-GCN structure is proposed for adjusting adjacency matrix weights adaptively, which is composed of the weight learning module and the classification module. The experimental results on simulation, public, and real-world datasets indicate that the proposed method possesses lower complexity, shorter reasoning time, and higher recognition accuracy, which holds potential for application in scenarios, where a timely response to GNSS interference attacks is necessary.