Deep Learning Models Applied in Automatic Modulation Classification of Radar Signals

Pedro De Figueiredo Abissamra, Sarah Leite, Renato Machado, Dimas Irion Alves

DOI: 10.14209/sbrt.2025.1571144222

Evento: XLIII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2025)

Keywords: AMC Convolutional Neural Networks Long Short-Term Memory Radar Signals

Abstract

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.

Download