{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T06:38:21Z","timestamp":1764225501616,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,2,13]],"date-time":"2019-02-13T00:00:00Z","timestamp":1550016000000},"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>A major burden of signal strength-based fingerprinting for indoor positioning is the generation and maintenance of a radio map, also known as a fingerprint database. Model-based radio maps are generated much faster than measurement-based radio maps but are generally not accurate enough. This work proposes a method to automatically construct and optimize a model-based radio map. The method is based on unsupervised learning, where random walks, for which the ground truth locations are unknown, serve as input for the optimization, along with a floor plan and a location tracking algorithm. No measurement campaign or site survey, which are labor-intensive and time-consuming, or inertial sensor measurements, which are often not available and consume additional power, are needed for this approach. Experiments in a large office building, covering over 1100 m2, resulted in median accuracies of up to 2.07 m, or a relative improvement of 28.6% with only 15 min of unlabeled training data.<\/jats:p>","DOI":"10.3390\/s19040752","type":"journal-article","created":{"date-parts":[[2019,2,14]],"date-time":"2019-02-14T03:21:46Z","timestamp":1550114506000},"page":"752","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["An Unsupervised Learning Technique to Optimize Radio Maps for Indoor Localization"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0185-5409","authenticated-orcid":false,"given":"Jens","family":"Trogh","sequence":"first","affiliation":[{"name":"Department of Information Technology, IMEC\u2014Ghent University, Ghent 9052, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8807-0673","authenticated-orcid":false,"given":"Wout","family":"Joseph","sequence":"additional","affiliation":[{"name":"Department of Information Technology, IMEC\u2014Ghent University, Ghent 9052, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luc","family":"Martens","sequence":"additional","affiliation":[{"name":"Department of Information Technology, IMEC\u2014Ghent University, Ghent 9052, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8879-5076","authenticated-orcid":false,"given":"David","family":"Plets","sequence":"additional","affiliation":[{"name":"Department of Information Technology, IMEC\u2014Ghent University, Ghent 9052, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1109\/TRO.2004.833793","article-title":"Revisiting trilateration for robot localization","volume":"21","author":"Thomas","year":"2005","journal-title":"IEEE Trans. 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