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Accurate automatic modulation recognition (AMR) of LPI radar signals is essential for electronic warfare decision\u2010making and offers critical insights for optimising algorithms in cognitive radio (CR). However, existing deep learning\u2010based AMR methods have exhibited significant performance degradation in low signal\u2010to\u2010noise ratio (SNR) conditions, making this task challenging. To address this issue, we propose a novel method for AMR of LPI radar signals in low SNR conditions, named the improved U\u2010Lite denoising and recognition network (IUDR\u2010Net). The method integrates an improved U\u2010Lite denoising network and a recognition network, combining a two\u2010stage training process with a joint loss function to achieve accurate modulation recognition. Experimental results demonstrate that the IUDR\u2010Net outperforms state\u2010of\u2010the\u2010art methods in low SNR conditions as well as in a lightweight design and strong robustness. In the additive white Gaussian noise environment, the IUDR\u2010Net achieves an average recognition accuracy of 92.5% at an SNR of \u221212\u00a0dB for 13 types of LPI radar modulation signals.<\/jats:p>","DOI":"10.1049\/cmu2.70147","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T12:12:32Z","timestamp":1773835952000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["IUDR\u2010Net: Improved U\u2010Lite Denoising and Recognition Network for Automatic Modulation Recognition of LPI Radar Signals"],"prefix":"10.1049","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7260-273X","authenticated-orcid":false,"given":"Haikun","family":"Fang","sequence":"first","affiliation":[{"name":"Information Engineering University  Zhengzhou China"}]},{"given":"Shiwen","family":"Chen","sequence":"additional","affiliation":[{"name":"Information Engineering University  Zhengzhou China"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"Information Engineering University  Zhengzhou China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2982-447X","authenticated-orcid":false,"given":"Chaopeng","family":"Wu","sequence":"additional","affiliation":[{"name":"Information Engineering University  Zhengzhou China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9908-3840","authenticated-orcid":false,"given":"Gangyin","family":"Sun","sequence":"additional","affiliation":[{"name":"Information Engineering University  Zhengzhou China"}]}],"member":"265","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2021.3130797"},{"key":"e_1_2_10_3_1","volume-title":"Detecting and Classifying Low Probability of Intercept Radar","author":"Pace P. 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