Research on Interference Detection and Identification Algorithms for Satellite Communication Systems

Satellite communication systems face various interference threats in complex electromagnetic environments, where traditional interference detection methods struggle to cope with the diversity of interference types and intricate signal characteristics. This paper proposes an intelligent interference detection and identification algorithm based on deep learning. By leveraging the advantages of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the proposed algorithm effectively classifies and identifies multiple interference signals. A dataset comprising five typical interference types was constructed, and a multi-layer feature extraction network architecture was designed to achieve accurate recognition of narrowband interference, broadband noise, impulse noise, frequency-sweeping interference, and multi-tone interference. Experimental results demonstrate that under signal-to-noise ratio (SNR) conditions greater than -10 dB, the proposed algorithm achieves a detection accuracy of 96.5% and a classification accuracy of 94.2%, representing improvements of 12.3% and 15.7% over traditional methods, respectively. The algorithm exhibits excellent real-time performance and robustness, providing effective technical support for enhancing the anti-interference capability of satellite communication systems.