Abstract
Radio-frequency interference (RFI) poses a critical challenge for modern high-precision Global Navigation Satellite System (GNSS) applications, as both intentional and unintentional interference can significantly degrade positioning accuracy and reliability. With increasingly sophisticated interference sources, robust and computationally efficient automatic recognition methods are required for next-generation GNSS receivers. Although deep learning approaches show strong potential for interference detection, their high computational cost often limits deployment in resource-constrained navigation hardware. This paper proposes a hybrid deep learning architecture for radio interference recognition in high-precision GNSSs. The framework employs a dual-branch design integrating complementary signal representations. A Self-Organizing Operational Neural Network (Self-ONN) extracts nonlinear temporal features from raw one-dimensional signals, while a Vision Mamba state-space model processes two-dimensional time-frequency spectrograms obtained via Short-Time Fourier Transform (STFT). The fused features enable accurate classification of diverse interference types with high computational efficiency. Experiments on a synthetic dataset demonstrate that the proposed model achieves 99.83% accuracy and F1-score, outperforming ResNet18, VGG16, and Vision Transformer while reducing computational complexity by up to 42% and improving inference speed by up to 35%, supporting its applicability for intelligent interference monitoring in GNSS receivers.