{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T06:48:13Z","timestamp":1767422893151,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,20]],"date-time":"2020-11-20T00:00:00Z","timestamp":1605830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Portugal 2020","award":["POCI-01-0247-FEDER-033479"],"award-info":[{"award-number":["POCI-01-0247-FEDER-033479"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Infrastructure-free Indoor Positioning Systems (IPS) are becoming popular due to their scalability and a wide range of applications. Such systems often rely on deployed Wi-Fi networks. However, their usability may be compromised, either due to scanning restrictions from recent Android versions or the proliferation of 5G technology. This raises the need for new infrastructure-free IPS independent of Wi-Fi networks. In this paper, we propose the use of magnetic field data for IPS, through Deep Neural Networks (DNN). Firstly, a dataset of human indoor trajectories was collected with different smartphones. Afterwards, a magnetic fingerprint was constructed and relevant features were extracted to train a DNN that returns a probability map of a user\u2019s location. Finally, two postprocessing methods were applied to obtain the most probable location regions. We asserted the performance of our solution against a test dataset, which produced a Success Rate of around 80%. We believe that these results are competitive for an IPS based on a single sensing source. Moreover, the magnetic field can be used as an additional information layer to increase the robustness and redundancy of current multi-source IPS.<\/jats:p>","DOI":"10.3390\/s20226664","type":"journal-article","created":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T01:28:48Z","timestamp":1606094928000},"page":"6664","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["An Infrastructure-Free Magnetic-Based Indoor Positioning System with Deep Learning"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1624-7716","authenticated-orcid":false,"given":"Let\u00edcia","family":"Fernandes","sequence":"first","affiliation":[{"name":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455\/461, 4200-135 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2554-3648","authenticated-orcid":false,"given":"Sara","family":"Santos","sequence":"additional","affiliation":[{"name":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455\/461, 4200-135 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9445-4809","authenticated-orcid":false,"given":"Mar\u00edlia","family":"Barandas","sequence":"additional","affiliation":[{"name":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455\/461, 4200-135 Porto, Portugal"},{"name":"Laborat\u00f3rio de Instrumenta\u00e7\u00e3o, Engenharia Biom\u00e9dica e F\u00edsica da Radia\u00e7\u00e3o (LIBPhys-UNL), Departamento de F\u00edsica, Faculdade de Ci\u00eancias e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8481-6079","authenticated-orcid":false,"given":"Duarte","family":"Folgado","sequence":"additional","affiliation":[{"name":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455\/461, 4200-135 Porto, Portugal"},{"name":"Laborat\u00f3rio de Instrumenta\u00e7\u00e3o, Engenharia Biom\u00e9dica e F\u00edsica da Radia\u00e7\u00e3o (LIBPhys-UNL), Departamento de F\u00edsica, Faculdade de Ci\u00eancias e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2695-4462","authenticated-orcid":false,"given":"Ricardo","family":"Leonardo","sequence":"additional","affiliation":[{"name":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455\/461, 4200-135 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4478-2476","authenticated-orcid":false,"given":"Ricardo","family":"Santos","sequence":"additional","affiliation":[{"name":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455\/461, 4200-135 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4234-5336","authenticated-orcid":false,"given":"Andr\u00e9","family":"Carreiro","sequence":"additional","affiliation":[{"name":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455\/461, 4200-135 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4022-7424","authenticated-orcid":false,"given":"Hugo","family":"Gamboa","sequence":"additional","affiliation":[{"name":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455\/461, 4200-135 Porto, Portugal"},{"name":"Laborat\u00f3rio de Instrumenta\u00e7\u00e3o, Engenharia Biom\u00e9dica e F\u00edsica da Radia\u00e7\u00e3o (LIBPhys-UNL), Departamento de F\u00edsica, Faculdade de Ci\u00eancias e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kunhoth, J., Karkar, A.G., Al-Maadeed, S., and Al-Ali, A. 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