GNSS blanket jamming classification algorithm based on spatial attention mechanism and residual shrinkage neural network

In the context of an increasingly complex electromagnetic environment, satellite navigation systems have become highly susceptible to jamming. Detecting and classifying jamming has thus become crucial for taking effective anti-jamming measures. This paper addresses the issue that the classification accuracy of blanket jamming declines drastically in low jamming-to-noise ratio (JNR) scenarios. To tackle this challenge, a novel algorithm is proposed that combines the spatial attention mechanism with a residual shrinkage neural network (RSN-SA) to classify ten types of blanket jamming, ranging from single jamming to convolutional compound jamming. Specifically, the proposed algorithm first employs the Fourier Synchrosqueezed Transform to extract time-frequency (TF) domain features from the original jamming signal, generating corresponding TF images. Then, the RSN-SA is employed to identify and classify these images effectively while minimizing the impact of noise-related features. This allows the main parts of the TF images to be focused on, resulting in higher recognition accuracy. Simulation results demonstrate that RSN-SA achieves close to 100% accuracy for six single blanket jamming signals. Moreover, compared with the other five algorithms, RSN-SA effectively enhances the classification accuracy of convolutional compound jamming signals in low JNR scenarios and improves the recognition stability in high JNR scenarios. Overall, the proposed algorithm provides a promising solution for classifying blanket jamming in satellite navigation systems with high accuracy and robustness.