{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:35:55Z","timestamp":1776890155000,"version":"3.51.2"},"reference-count":26,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the widespread use of multifunction radars (MFRs), it is hard for the traditional radar signal recognition technology to meet the needs of current electronic intelligence systems. For signal recognition of an MFR, it is necessary to identify not only the type or individual of the emitter but also its current state. Existing methods identify MFR states through hierarchical modeling, but most of them rely heavily on prior information. In the paper, we focus on the MFR state recognition with actual intercepted MFR signals and develop it by introducing recurrent neural networks (RNNs) of deep learning into the modeling of MFR signals. According to the layered MFR signal architecture, we propose a novel end-to-end state recognition approach with two RNNs\u2019 connections. This approach makes full use of RNNs\u2019 ability to directly tackle corrupted data and automatically learn the features from input data. So, it is practical and less dependent on prior information. In addition, the hierarchical modeling method applied to the end-to-end network effectively restricts the scale of the end-to-end model so that the model can be trained with a small amount of data. Simulation results on a real MFR show the excellent recognition performance of our end-to-end approach with little prior information.<\/jats:p>","DOI":"10.3390\/s22134980","type":"journal-article","created":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T20:59:18Z","timestamp":1656968358000},"page":"4980","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["An End-to-End Deep Learning Approach for State Recognition of Multifunction Radars"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8743-1684","authenticated-orcid":false,"given":"Xinsong","family":"Xu","sequence":"first","affiliation":[{"name":"College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daping","family":"Bi","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jifei","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1049\/iet-rsn.2018.5202","article-title":"Radar emitters classification and clustering with a scale mixture of normal distributions","volume":"1","author":"Revillon","year":"2019","journal-title":"IET Radar Sonar Navig."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1049\/iet-rsn.2016.0632","article-title":"Radar emitter classification for large data set based on weighted-xgboost","volume":"8","author":"Chen","year":"2017","journal-title":"IET Radar Sonar Navig."},{"key":"ref_3","first-page":"581","article-title":"Specific emitter identification based on variational mode decomposition and spectral features in single hop and relaying scenarios","volume":"3","author":"Satija","year":"2018","journal-title":"IEEE Trans. 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