LPI radar signal recognition with U?Net-based denoising

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 U²-Net for LPI radar signals, followed by modulation classification using a Convolutional Neural Network (CNN). We further compare the performance of U²-Net with other denoising models such as UNet and denoising autoencoder. Experimental results demonstrate that the U²-Net outperforms other methods, achieving over 90% classification accuracy for signals with a signal-to-noise ratio above -14dB