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Changes in various environmental conditions and signal drift can cause different probability distributions between the data sets. Existing positioning algorithms cannot guarantee stable accuracy when facing these issues, resulting in dramatic reduction and the infeasibility of the positioning accuracy of indoor location algorithms. Considering these restrictions, domain adaptation technology in transfer learning has proven to be a promising solution in past research in terms of solving the inconsistent probability distribution problems. However, most localization algorithms based on transfer learning do not perform well because they only learn a shallow representation feature, which can only slightly reduce the domain discrepancy. Based on the deep network and its strong feature extraction ability, it can learn more transferable features for domain adaptation and achieve better domain adaptation effects. A Deep Joint Mean Distribution Adaptation Network (DJMDAN) is proposed to align the global domain and relevant subdomain distributions of activations in multiple domain-specific layers across domains to achieve domain adaptation. The test results demonstrate that the performance of the proposed method outperforms the comparison algorithm in indoor positioning applications.<\/jats:p>","DOI":"10.3390\/s23239334","type":"journal-article","created":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T10:41:51Z","timestamp":1700649711000},"page":"9334","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Transfer Learning for Indoor Localization Algorithm Based on Deep Domain Adaptation"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4460-3722","authenticated-orcid":false,"given":"Jiahao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifu","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hainan","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junxiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Technology Innovation Department of Tianfu Co-Innovation Center, University of Electronic Science and Technology of China, Chengdu 610000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109472","DOI":"10.1016\/j.buildenv.2022.109472","article-title":"Plug-Mate: An IoT-based occupancy-driven plug load management system in smart buildings","volume":"223","author":"Tekler","year":"2022","journal-title":"Build. 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