Deep Learning Models Applied in Automatic Modulation Classification of Radar Signals

Artificial Intelligence in Electronic Warfare has gained prominence, particularly for Automatic Modulation Clas- sification tasks. Deep learning methods have demonstrated ro- bustness and high accuracy in addressing this challenge. This study proposed and tested Long Short-Term Memory and Convo- lutional Neural Network architectures for Automatic Modulation Classification in radar signals. The LSTM model achieved 90% accuracy in classifying eleven modulation types at -2.66 dB SNR, while the CNN model reached the same accuracy at 1.50 dB SNR. Although the LSTM outperformed the CNN, it required higher computational resources and longer latency.