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The precision of the current map-matching algorithms is subject to the poor ability of integrated sensor-based trajectory estimation and the efficient combination of pedestrian motion information and the pedestrian indoor network. This paper proposes an autonomous multi-floor localization framework based on smartphone-integrated sensors and pedestrian network matching (ML-ISNM). A robust data and model dual-driven pedestrian trajectory estimator is proposed for accurate integrated sensor-based positioning under different handheld modes and disturbed environments. A bi-directional long short-term memory (Bi-LSTM) network is further applied for floor identification using extracted environmental features and pedestrian motion features, and further combined with the indoor network matching algorithm for acquiring accurate location and floor observations. In the multi-source fusion procedure, an error ellipse-enhanced unscented Kalman filter is developed for the intelligent combination of a trajectory estimator, human motion constraints, and the extracted pedestrian network. Comprehensive experiments indicate that the presented ML-ISNM achieves autonomous and accurate multi-floor positioning performance in complex and large-scale urban buildings. The final evaluated average localization error was lower than 1.13 m without the assistance of wireless facilities or a navigation database.<\/jats:p>","DOI":"10.3390\/rs15112933","type":"journal-article","created":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T02:18:29Z","timestamp":1685931509000},"page":"2933","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Autonomous Multi-Floor Localization Based on Smartphone-Integrated Sensors and Pedestrian Indoor Network"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4383-0974","authenticated-orcid":false,"given":"Chaoyang","family":"Shi","sequence":"first","affiliation":[{"name":"School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430079, China"}]},{"given":"Wenxin","family":"Teng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong 999077, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3529-585X","authenticated-orcid":false,"given":"Yue","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7083-6001","authenticated-orcid":false,"given":"Liang","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6683-2342","authenticated-orcid":false,"given":"Ruizhi","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}]},{"given":"Qingquan","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11827","DOI":"10.1109\/JIOT.2021.3132023","article-title":"H-WPS: Hybrid Wireless Positioning System Using an Enhanced Wi-Fi FTM\/RSSI\/MEMS Sensors Integration Approach","volume":"9","author":"Yu","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"117095","DOI":"10.1016\/j.eswa.2022.117095","article-title":"Performance analysis of fingerprinting indoor positioning methods with, B.L.E","volume":"202","author":"Aranda","year":"2022","journal-title":"Expert Syst. 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