Deep learning-based GNSS composite jamming detection and recognition technology - Liu, Chenxuan; Ren, Binbin; Xie, Yuchen; Chen, Feiqiang (Frontiers in Signal Processing, 2025)
As the interference environment of Global Navigation Satellite Systems (GNSS) becomes increasingly complex and diverse, real-time and precise interference detection and identification technologies are crucial for enhancing the anti-interference capabilities of receivers. However, most existing interference detection and identification methods focus on single interference types, with limited research on composite interference and a lack of quantitative conclusions. Therefore, this study investigates composite interference detection and identification techniques using deep learning methods, improving the system’s capability to detect and identify composite interference. This paper first constructs single interference model and composite interference model, proposes three signal preprocessing methods, and generates corresponding image datasets. Subsequently, the interference detection and identification performance under different signal preprocessing methods is analyzed using the ResNet-18 deep learning neural network. The optimal signal preprocessing method is identified, and quantitative conclusions are obtained. Finally, a lightweight network, LcxNet-Fusion, is designed, which significantly reduces the number of network parameters and forward processing time while maintaining an acceptable level of accuracy reduction. Results show that among the time-frequency 2D diagrams, power spectral diagrams, and histograms generated by signal preprocessing, the time-frequency diagram yields the best detection and identification performance. When the detection rate reaches 90%, the jamming-to-noise ratio (JNR) sensitivity of the time-frequency diagram is −20 dB; when the identification rate reaches 90%, the JNR sensitivity of the time-frequency diagram is −13 dB. On the Tesla V100 GPU, the LcxNet-Fusion network has 24.32 MB parameters, an 43% reduction compared to the ResNet-18 network, with a forward processing time of 1.25 s, reducing by 15%. This work holds promising prospects in the field of interference detection and identification for GNSS systems under complex electromagnetic environments.