LPI radar signal recognition with U-Net-based denoising

Siho Lee, Haewoon Nam

Abstract: Low Probability of Intercept (LPI) radar signals play a vital role in electronic warfare by maintaining informational superiority. Classifying these LPI radar waveforms is a key capability but remains a challenging task due to strong noise interference. Traditional signal processing techniques often show limitations in effectively removing complex noise signals. While deep learning-based modulation classification has exhibited superior performance, its effectiveness is compromised in the presence of significant noise. In this study, we propose a deep learning-based denoising method using the U 2 -Net for LPI radar signals, followed by modulation classification using a Convolutional Neural Network (CNN). We further compare the performance of U 2 -Net with other denoising models such as U-Net and denoising autoencoder. Experimental results demonstrate that the U 2 -Net outperforms other methods, achieving over 90% classification accuracy for signals with a signal-to-noise ratio above -14dB.