{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:21:57Z","timestamp":1753885317388,"version":"3.41.2"},"reference-count":22,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Model. Simul. Sci. Comput."],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:p> Identifying source signals from mixed waves presents a significant challenge, as isolating the original signal is typically difficult. This paper presents a novel feature descriptor, termed Implicit Mapping Relations (IMRs). Unlike conventional numerical features, which are challenging to extract from mixed signals, IMRs exploit the mapping relationships among truncated segments of training samples. Furthermore, the proposed method does not depend on end-to-end training with mixed signals, thereby addressing the reliance on mixed signal training data that is prevalent in traditional approaches. Additionally, by mimicking the functionality of human implicit memory, the method is capable of recognizing known components even in the presence of unknown signal interference. This constitutes an enhancement compared to dictionary learning algorithms, which generally encounter difficulties in adapting to unknown components. Utilizing the most stringent evaluation criteria, both false alarms and missed detections are classified as identification errors. The recognition accuracy exceeds 70% on the specified dataset when the maximum number of mixture components is set to six. <\/jats:p><jats:p> To assess the algorithm\u2019s performance in the presence of interference from novel components, specific waveforms were systematically excluded from the training set and classified as unknown signals, while the testing set was retained in its original form. The proposed method demonstrated an increased correct recognition probability of 90%. This enhancement in recognition accuracy can be attributed to the algorithm not requiring identification of unknown components. By leveraging IMRs specific to each category for classification, the proposed method effectively mitigates disruptions caused by unknown components. <\/jats:p>","DOI":"10.1142\/s1793962325410247","type":"journal-article","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:06:28Z","timestamp":1749773188000},"source":"Crossref","is-referenced-by-count":0,"title":["An implicit mapping-based method for mixture signal recognition"],"prefix":"10.1142","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5487-7372","authenticated-orcid":false,"given":"Yu","family":"Ma","sequence":"first","affiliation":[{"name":"The Institute of System Engineering, AMS, PLA, Beijing, 100191, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2830-9539","authenticated-orcid":false,"given":"Shafei","family":"Wang","sequence":"additional","affiliation":[{"name":"The Institute of System Engineering, AMS, PLA, Beijing, 100191, P. R. 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