Electromagnetic Signal Anomaly Detection and Classification Methods Based on Deep Learning

With the rapid development of communication technology, the complexity of the electromagnetic environment is increasing, making the detection and classification of electromagnetic signal anomalies a crucial task for ensuring communication quality and security. Deep learning technologies offer new perspectives and methodologies for addressing this issue. However, traditional models often display limited adaptability in complex electromagnetic scenarios, particularly under coherent noise and multi-source interference, and they require extensive labeled data. To overcome these challenges, this paper proposes a novel approach for electromagnetic signal anomaly detection and classification. Initially, an adaptive mechanism for coherent noise suppression is studied to enhance detection performance in complex environments. Subsequently, by integrating deep Q-net (DQN) technology, an intelligent recognition and classification strategy is developed. Through self-learning, this method effectively identifies and classifies abnormal signals, reducing reliance on large volumes of labeled data while improving the system's adaptability to dynamic environments and processing accuracy. This research demonstrates the potential application of deep learning in modern electromagnetic signal processing and holds significant implications for advancing electromagnetic environment monitoring and management technologies.