{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T05:39:18Z","timestamp":1780378758610,"version":"3.54.1"},"reference-count":34,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T00:00:00Z","timestamp":1700092800000},"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>Global Navigation Satellite Systems (GNSSs) are nowadays the prevailing technology for positioning and navigation. However, with the roll-out of 5G technology, there is a shift towards \u2018hybrid positioning\u2019: indeed, 5G time-of-arrival (ToA) measurements can provide additional ranging for positioning, especially in environments where few GNSS satellites are visible. This work reports a preliminary analysis, the processing, and the results of field measurements collected as part of the GINTO5G project funded by ESA\u2019s EGEP programme. The data used in this project were shared by the European Space Agency (ESA) with the DICA of Politecnico di Milano as part of a collaboration within the ESALab@PoliMi research framework established in 2022 between the two organizations. The ToA data were collected during a real-world measurement campaign and they cover a wide range of user environments, such as indoor areas, outdoor open sky, and outdoor obstructed scenarios. Within the test area, eleven self-made replica 5G base stations were set up. A trolley, carrying a self-made 5G receiver and a data storage unit, was moved along predefined trajectories; the trolley\u2019s accurate trajectories were determined by a total station, which provided benchmark positions. In the present work, the 5G data are processed using the least squares method, testing and comparing different strategies. Therefore, the primary goal is to evaluate algorithms for position determination of a user based on 5G observations, and to empirically assess their accuracy. The results obtained are promising, with positional accuracy ranging from decimeters to a few meters in the worst cases.<\/jats:p>","DOI":"10.3390\/s23229222","type":"journal-article","created":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T08:19:43Z","timestamp":1700122783000},"page":"9222","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["5G Positioning: An Analysis of Early Datasets"],"prefix":"10.3390","volume":"23","author":[{"given":"Chiara","family":"Pileggi","sequence":"first","affiliation":[{"name":"DICA Politecnico di Milano, Piazza Leonardo Da Vinci 32, 20133 Milano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2944-9613","authenticated-orcid":false,"given":"Florin Catalin","family":"Grec","sequence":"additional","affiliation":[{"name":"DICA Politecnico di Milano, Piazza Leonardo Da Vinci 32, 20133 Milano, Italy"},{"name":"European Space Agency, Keplerlaan 1, 2201AZ Noordwijk, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1179-358X","authenticated-orcid":false,"given":"Ludovico","family":"Biagi","sequence":"additional","affiliation":[{"name":"DICA Politecnico di Milano, Piazza Leonardo Da Vinci 32, 20133 Milano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Leick, A., Rapoport, L., and Tatarnikov, D. 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