{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:36:42Z","timestamp":1760240202323,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,4,9]],"date-time":"2019-04-09T00:00:00Z","timestamp":1554768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Center for Research and Developmnet, Poland","award":["POIR.04.01.04-00-0005\/17"],"award-info":[{"award-number":["POIR.04.01.04-00-0005\/17"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>There are multiple available technologies to find the location of a mobile device, such as the Global Positioning System (GPS), Bluetooth Low-Energy beacons (BLE), and Wireless LAN (WLAN) localization. We propose a novel method to estimate the location of a moving device by aggregating information from multiple positioning systems into a single, more precise location estimation. The aggregated location is calculated as the place in which the product of the probability density functions (PDF) of individual methods has the maximum value. The experimental probability density functions of the three analyzed technologies are fitted by gamma distributions based on error histograms found in the literature and measurement data. The location measurements of the individual technologies are provided at different time instants, so the weighted product of the PDFs is used to improve aggregation accuracy. The discrete event-simulation model was used to evaluate the aggregation method with the Gauss\u2013Markov mobility model. Simulations demonstrated that the calculated aggregated location was more accurate than any of the methods taken as the input, and average error was decreased by almost 13% compared to an arithmetic mean of the three considered localization methods, and by more than 36% compared to the single method with the highest accuracy.<\/jats:p>","DOI":"10.3390\/s19071694","type":"journal-article","created":{"date-parts":[[2019,4,10]],"date-time":"2019-04-10T03:47:36Z","timestamp":1554868056000},"page":"1694","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Aggregation of GPS, WLAN, and BLE Localization Measurements for Mobile Devices in Simulated Environments"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0201-6220","authenticated-orcid":false,"given":"Kamil","family":"Ksi\u0105\u017cek","sequence":"first","affiliation":[{"name":"Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul. Baltycka 5, 44-100 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6221-4790","authenticated-orcid":false,"given":"Krzysztof","family":"Grochla","sequence":"additional","affiliation":[{"name":"Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul. Baltycka 5, 44-100 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rajalakshmi, K., and Goyal, M. (2018). Location-Based Services: Current State of The Art and Future Prospects. Optical and Wireless Technologies, Springer.","DOI":"10.1007\/978-981-10-7395-3_69"},{"key":"ref_2","unstructured":"Cisco Visual Networking Index (2019, January 30). Forecast and Methodology, 2016\u20132021. September 2017. 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