{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T02:05:50Z","timestamp":1775873150206,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,20]],"date-time":"2022-02-20T00:00:00Z","timestamp":1645315200000},"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>This paper studies the problem of detecting human beings in non-line-of-sight (NLOS) conditions using an ultra-wideband radar. We perform an extensive measurement campaign in realistic environments, considering different body orientations, the obstacles\u2019 materials, and radar\u2013obstacle distances. We examine two main scenarios according to the radar position: (i) placed on top of a mobile cart; (ii) handheld at different heights. We empirically analyze and compare several input representations and machine learning (ML) methods\u2014supervised and unsupervised, symbolic and non-symbolic\u2014according to both their accuracy in detecting NLOS human beings and their adaptability to unseen cases. Our study proves the effectiveness and flexibility of modern ML techniques, avoiding environment-specific configurations and benefiting from knowledge transference. Unlike traditional TLC approaches, ML allows for generalization, overcoming limits due to unknown or only partially known observation models and insufficient labeled data, which usually occur in emergencies or in the presence of time\/cost constraints.<\/jats:p>","DOI":"10.3390\/s22041656","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T08:34:47Z","timestamp":1645432487000},"page":"1656","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Human Being Detection from UWB NLOS Signals: Accuracy and Generality of Advanced Machine Learning Models"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3663-7877","authenticated-orcid":false,"given":"Gianluca","family":"Moro","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering (DISI), University of Bologna, 47521 Cesena, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0568-6227","authenticated-orcid":false,"given":"Federico","family":"Di Luca","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic, and Information Engineering (DEI), University of Bologna, 47521 Cesena, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5994-1310","authenticated-orcid":false,"given":"Davide","family":"Dardari","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic, and Information Engineering (DEI), University of Bologna, 47521 Cesena, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9845-0231","authenticated-orcid":false,"given":"Giacomo","family":"Frisoni","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering (DISI), University of Bologna, 47521 Cesena, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4257","DOI":"10.1109\/TAP.2011.2164214","article-title":"Propagation parameter estimation, modeling and measurements for ultrawideband MIMO radar","volume":"59","author":"Salmi","year":"2011","journal-title":"IEEE Trans. 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