{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:08:06Z","timestamp":1778083686976,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T00:00:00Z","timestamp":1668038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971439"],"award-info":[{"award-number":["61971439"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702543"],"award-info":[{"award-number":["61702543"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the advance of the Internet of things (IoT), localization is essential in varied services. In urban scenarios, multiple transmitters localization is faced with challenges such as nonline-of-sight (NLOS) propagation and limited deployment of sensors. To this end, this paper proposes the MT-GCNN (Multi-Task Gated Convolutional Neural Network), a novel multiple transmitters localization scheme based on deep multi-task learning, to learn the NLOS propagation features and achieve the localization. The multi-task learning network decomposes the problem into a coarse localization task and a fine correction task. In particular, the MT-GCNN uses an improved gated convolution module to extract features from sparse sensing data more effectively. In the training stage, a joint loss function is proposed to optimize the two branches of tasks. In the testing stage, the well-trained MT-GCNN model predicts the classified grids and corresponding biases jointly to improve the overall performance of localization. In the urban scenarios challenged by NLOS propagation and sparse deployment of sensors, numerical simulations demonstrate that the proposed MT-GCNN framework has more accurate and robust performance than other algorithms.<\/jats:p>","DOI":"10.3390\/s22228674","type":"journal-article","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T21:22:02Z","timestamp":1668115322000},"page":"8674","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["MT-GCNN: Multi-Task Learning with Gated Convolution for Multiple Transmitters Localization in Urban Scenarios"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6772-1839","authenticated-orcid":false,"given":"Wenyu","family":"Wang","sequence":"first","affiliation":[{"name":"College of Communications Engineering, Army Engineering University of PLA, Nanjing 210001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4716-1437","authenticated-orcid":false,"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Communications Engineering, Army Engineering University of PLA, Nanjing 210001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5441-7229","authenticated-orcid":false,"given":"Zhen","family":"Huang","sequence":"additional","affiliation":[{"name":"Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100089, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1023-0585","authenticated-orcid":false,"given":"Baozhu","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100089, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Communications Engineering, Army Engineering University of PLA, Nanjing 210001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaixin","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Communications Engineering, Army Engineering University of PLA, Nanjing 210001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1484","DOI":"10.1109\/TVT.2013.2244928","article-title":"Analysis of Wireless Localization in Nonline-of-Sight Conditions","volume":"62","author":"Liu","year":"2013","journal-title":"IEEE Trans. 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