{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T15:59:25Z","timestamp":1781279965993,"version":"3.54.1"},"reference-count":27,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T00:00:00Z","timestamp":1694736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CETC Key Laboratory of Data Link Technology","award":["CLDL-20202411"],"award-info":[{"award-number":["CLDL-20202411"]}]},{"name":"CETC Key Laboratory of Data Link Technology","award":["YYJC022022014"],"award-info":[{"award-number":["YYJC022022014"]}]},{"name":"SongShan Laboratory","award":["CLDL-20202411"],"award-info":[{"award-number":["CLDL-20202411"]}]},{"name":"SongShan Laboratory","award":["YYJC022022014"],"award-info":[{"award-number":["YYJC022022014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In complex battlefield environments, flying ad-hoc network (FANET) faces challenges in manually extracting communication interference signal features, a low recognition rate in strong noise environments, and an inability to recognize unknown interference types. To solve these problems, one simple non-local correction shrinkage (SNCS) module is constructed. The SNCS module modifies the soft threshold function in the traditional denoising method and embeds it into the neural network, so that the threshold can be adjusted adaptively. Local importance-based pooling (LIP) is introduced to enhance the useful features of interference signals and reduce noise in the downsampling process. Moreover, the joint loss function is constructed by combining the cross-entropy loss and center loss to jointly train the model. To distinguish unknown class interference signals, the acceptance factor is proposed. Meanwhile, the acceptance factor-based unknown class recognition simplified non-local residual shrinkage network (AFUCR-SNRSN) model with the capacity for both known and unknown class recognition is constructed by combining AFUCR and SNRSN. Experimental results show that the recognition accuracy of the AFUCR-SNRSN model is the highest in the scenario of a low jamming to noise ratio (JNR). The accuracy is increased by approximately 4\u20139% compared with other methods on known class interference signal datasets, and the recognition accuracy reaches 99% when the JNR is \u22126 dB. At the same time, compared with other methods, the false positive rate (FPR) in recognizing unknown class interference signals drops to 9%.<\/jats:p>","DOI":"10.3390\/s23187909","type":"journal-article","created":{"date-parts":[[2023,9,17]],"date-time":"2023-09-17T23:57:46Z","timestamp":1694995066000},"page":"7909","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Improved Deep Residual Shrinkage Network for Intelligent Interference Recognition with Unknown Interference"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3099-9946","authenticated-orcid":false,"given":"Xiaojun","family":"Wu","sequence":"first","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"Shaanxi Joint Laboratory of Artificial Intelligence, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yibo","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"Shaanxi Joint Laboratory of Artificial Intelligence, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daolong","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Technology on Datalink, China Electronics Technology Group Corporation (CETC), 20th Institute, Xi\u2019an 710068, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haitao","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaya","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"Shaanxi Joint Laboratory of Artificial Intelligence, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanbing","family":"Li","sequence":"additional","affiliation":[{"name":"Songshan Laboratory, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6345","DOI":"10.1109\/TCOMM.2021.3088898","article-title":"Secrecy-energy efficient hybrid beamforming for satellite-terrestrial integrated networks","volume":"69","author":"Lin","year":"2021","journal-title":"IEEE Trans. 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