{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T05:27:29Z","timestamp":1785821249185,"version":"3.56.0"},"reference-count":41,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,27]],"date-time":"2021-08-27T00:00:00Z","timestamp":1630022400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["2020K000081"],"award-info":[{"award-number":["2020K000081"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In a Wi-Fi indoor positioning system (IPS), the performance of the IPS depends on the channel state information (CSI), which is often limited due to the multipath fading effect, especially in indoor environments involving multiple non-line-of-sight propagation paths. In this paper, we propose a novel IPS utilizing trajectory CSI observed from predetermined trajectories instead of the CSI collected at each stationary location; thus, the proposed method enables all the CSI along each route to be continuously encountered in the observation. Further, by using a generative adversarial network (GAN), which helps enlarge the training dataset, the cost of trajectory CSI collection can be significantly reduced. To fully exploit the trajectory CSI\u2019s spatial and temporal information, the proposed IPS employs a deep learning network of a one-dimensional convolutional neural network\u2013long short-term memory (1DCNN-LSTM). The proposed IPS was hardware-implemented, where digital signal processors and a universal software radio peripheral were used as a modem and radio frequency transceiver, respectively, for both access point and mobile device of Wi-Fi. We verified that the proposed IPS based on the trajectory CSI far outperforms the state-of-the-art IPS based on the CSI collected from stationary locations through extensive experimental tests and computer simulations.<\/jats:p>","DOI":"10.3390\/s21175776","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T22:58:15Z","timestamp":1630450695000},"page":"5776","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Deep-Learning-Based Wi-Fi Indoor Positioning System Using Continuous CSI of Trajectories"],"prefix":"10.3390","volume":"21","author":[{"given":"Zhongfeng","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Hanyang University, Seoul 04763, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minjae","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Hanyang University, Seoul 04763, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seungwon","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Hanyang University, Seoul 04763, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1109\/JSAC.2020.2980800","article-title":"Adaptive Positioning Systems Based on Multiple Wireless Interfaces for Industrial IoT in Harsh Manufacturing Environments","volume":"38","author":"Batalla","year":"2020","journal-title":"IEEE J. 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