{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:13:21Z","timestamp":1760242401163,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2017,8,4]],"date-time":"2017-08-04T00:00:00Z","timestamp":1501804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Indoor localization and indoor pedestrian navigation is an active field of research with increasing attention. As of today, many systems will run on commercial smartphones, but most of them still rely on fingerprinting, which demands high setup and maintenance times. Alternatives, such as simple signal strength prediction models, provide fast setup times, but often do not provide the accuracy required for use cases like indoor navigation or location-based services. While more complex models provide an increased accuracy by including architectural knowledge about walls and other obstacles, they often require additional computation during runtime and demand prior knowledge during setup. Within this work, we will thus focus on simple, easy to set up models and evaluate their performance compared to real-world measurements. The evaluation ranges from a fully-empiric, instant setup, given that the transmitter locations are well known, to a highly optimized scenario based on some reference measurements within the building. Furthermore, we will propose a new signal strength prediction model as a combination of several simple ones. This tradeoff increases accuracy with only minor additional computations. All of the optimized models are evaluated within an actual smartphone-based indoor localization system. This system uses the phone\u2019s Wi-Fi, barometer and IMU to infer the pedestrian\u2019s current location via recursive density estimation based on particle filtering. We will show that while a 100% empiric parameter choice for the model already provides enough accuracy for many use cases, a small number of reference measurements is enough to dramatically increase such a system\u2019s performance.<\/jats:p>","DOI":"10.3390\/ijgi6080233","type":"journal-article","created":{"date-parts":[[2017,8,4]],"date-time":"2017-08-04T11:07:08Z","timestamp":1501844828000},"page":"233","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["On Wi-Fi Model Optimizations for Smartphone-Based Indoor Localization"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4698-8232","authenticated-orcid":false,"given":"Frank","family":"Ebner","sequence":"first","affiliation":[{"name":"Faculty of Computer Science and Business Information Systems, University of Applied Sciences W\u00fcrzburg-Schweinfurt, W\u00fcrzburg 97074, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8249-8783","authenticated-orcid":false,"given":"Toni","family":"Fetzer","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Business Information Systems, University of Applied Sciences W\u00fcrzburg-Schweinfurt, W\u00fcrzburg 97074, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2573-3529","authenticated-orcid":false,"given":"Frank","family":"Deinzer","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Business Information Systems, University of Applied Sciences W\u00fcrzburg-Schweinfurt, W\u00fcrzburg 97074, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcin","family":"Grzegorzek","sequence":"additional","affiliation":[{"name":"Pattern Recognition Group, University of Siegen, Siegen 57076, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"K\u00f6ping, L., Grzegorzek, M., and Deinzer, F. 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