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Existing methods for radar signal identification require labeled samples and focus mainly on the classification of normal signals. However, in practice, anomalous samples (multipath interference signals) may be scarce and highly imbalanced (i.e., mostly normal samples). To address this problem, we propose a deep anomaly detection with attention (DADA) for semisupervised detection of multipath radar signals. The method transforms radar signals into time\u2013frequency images and is trained exclusively on normal samples. The autoencoder architecture is extended with a feature extractor network to capture latent sample features. CBAM attention is introduced to improve feature extraction. By learning the distribution of normal samples in high\u2010dimensional image space and low\u2010dimensional feature space, a two\u2010dimensional feature space representing normal samples is constructed. A one\u2010class SVM then learns the boundary of normal samples for anomaly detection. Extensive experiments on radar signal datasets validate the effectiveness of the proposed approach.<\/jats:p>","DOI":"10.1049\/2024\/5026821","type":"journal-article","created":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T14:05:07Z","timestamp":1705154707000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Anomaly Detection with Attention (DADA): A Novel Approach for Identifying Multipath Interference in Radar Signals"],"prefix":"10.1049","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0637-0390","authenticated-orcid":false,"given":"Kang","family":"Yan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weidong","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingkun","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7772-482X","authenticated-orcid":false,"given":"Zhenhua","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pucha","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4990-5073","authenticated-orcid":false,"given":"Ligang","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2024,1,13]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1049\/ip-rsn:19990264"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.4156\/jcit.vol6.issue11.23"},{"key":"e_1_2_9_3_2","first-page":"477","article-title":"Intra-pulse feature analysis of radar emitter signals","volume":"23","author":"Zhang G.","year":"2004","journal-title":"Journal of Infrared and Millimeter Waves"},{"key":"e_1_2_9_4_2","doi-asserted-by":"crossref","unstructured":"FuQ. 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