碩士 / 國立臺灣科技大學 / 電子工程系 / 105 / Nowadays, there is an increasing demand for indoor localization and navigation services with readily available Commercial off-the-shelf (COTS) devices, such as smartphones or wearable devices. Many applications on smartphones exploit different techniques and input for positioning. Most of these systems rely on Received Signal Strengths (RSSs) from indoor wireless emitting devices. However, the accuracy of indoor position systems is easily affected by signal interference in realistic situations.
In this paper, we propose and compare several indoor localization and navigation systems using Pedestrian Dead Reckoning (PDR), fingerprinting, ensemble machine learning, and hybrid systems, in order to find schemes that can be easily and cheaply applied to COTS devices.
| Identifer | oai:union.ndltd.org:TW/105NTUS5428120 |
| Date | January 2017 |
| Creators | JEN-CHIEH HSU, 許任捷 |
| Contributors | Jenq-Shiou Leu, 呂政修 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 69 |
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