The demand of Indoor Location Based Services LBS has increased over the past years as smart phone market expands. As a result, there's a growing interest in developing efficient and reliable indoor positioning systems for mobile devices. Wi-Fi signal strength fingerprint-based approaches attract more and more attention due to the wide deployment of Wi-Fi access points. Indoor positioning problem using Wi-Fi signal fingerprints can be viewed as a machine learning task to be solved mathematically. This thesis proposes an efficient and reliable Wi-Fi real-time indoor positioning system using machine learning algorithms. The proposed positioning system, together with a location server equipped with the same algorithms, are tested and evaluated in several indoor scenarios. Simulation and testing results show that the proposed system is a feasible LBS solution.
Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OTU.1807/43382 |
Date | 12 December 2013 |
Creators | Yu, Yibo |
Contributors | Valaee, Shahrokh |
Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
Language | en_ca |
Detected Language | English |
Type | Thesis |
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