Machine Learning Approaches for EMSO Signal Classification

This technical report discusses various machine learning methods for electromagnetic spectrum operation (EMSO) signal classification, focusing on communication and radar signals. It begins by outlining the importance of automated signal classification in both civilian and military applications, emphasizing the need for efficient and accurate signal identification. The report details traditional methods, such as decision trees, and contrasts them with machine learning approaches that offer enhanced flexibility and scalability. It describes the use of neural networks, particularly convolutional neural networks (CNN) and residual networks (ResNet), for communication signal classification, highlighting their performance metrics and the significance of confusion matrices in evaluating classification accuracy. Additionally, the report covers the application of Long-Short-Term Memory (LSTM) networks for radar signal classification, detailing the dataset used and the architecture of the LSTM model. The findings indicate that both CNN and ResNet models exhibit robustness, with ResNet demonstrating superior performance in high signal-to-noise ratio scenarios.