{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T06:24:24Z","timestamp":1761719064238,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T00:00:00Z","timestamp":1629244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["299099","338224","328214","334197"],"award-info":[{"award-number":["299099","338224","328214","334197"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Received signal strength (RSS) changes of static wireless nodes can be used for device-free localization and tracking (DFLT). Most RSS-based DFLT systems require access to calibration data, either RSS measurements from a time period when the area was not occupied by people, or measurements while a person stands in known locations. Such calibration periods can be very expensive in terms of time and effort, making system deployment and maintenance challenging. This paper develops an Expectation-Maximization (EM) algorithm based on Gaussian smoothing for estimating the unknown RSS model parameters, liberating the system from supervised training and calibration periods. To fully use the EM algorithm\u2019s potential, a novel localization-and-tracking system is presented to estimate a target\u2019s arbitrary trajectory. To demonstrate the effectiveness of the proposed approach, it is shown that: (i) the system requires no calibration period; (ii) the EM algorithm improves the accuracy of existing DFLT methods; (iii) it is computationally very efficient; and (iv) the system outperforms a state-of-the-art adaptive DFLT system in terms of tracking accuracy.<\/jats:p>","DOI":"10.3390\/s21165549","type":"journal-article","created":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T22:51:00Z","timestamp":1629327060000},"page":"5549","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Unsupervised Learning in RSS-Based DFLT Using an EM Algorithm"],"prefix":"10.3390","volume":"21","author":[{"given":"Ossi","family":"Kaltiokallio","sequence":"first","affiliation":[{"name":"Unit of Electrical Engineering, Tampere University, 33720 Tampere, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roland","family":"Hostettler","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Uppsala University, 75237 Uppsala, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7794-2763","authenticated-orcid":false,"given":"H\u00fcseyin","family":"Yi\u011fitler","sequence":"additional","affiliation":[{"name":"Department of Communications and Networking, Aalto University, 02150 Espoo, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mikko","family":"Valkama","sequence":"additional","affiliation":[{"name":"Unit of Electrical Engineering, Tampere University, 33720 Tampere, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/MIS.2015.63","article-title":"Ambient Assisted Living [Guest editors\u2019 introduction]","volume":"30","author":"Monekosso","year":"2015","journal-title":"IEEE Intell. 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