| DC 欄位 |
值 |
語言 |
| DC.contributor | 資訊工程學系 | zh_TW |
| DC.creator | 李宜庭 | zh_TW |
| DC.creator | Yi-Ting Li | en_US |
| dc.date.accessioned | 2013-7-30T07:39:07Z | |
| dc.date.available | 2013-7-30T07:39:07Z | |
| dc.date.issued | 2013 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=995202062 | |
| dc.contributor.department | 資訊工程學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 室內定位已成為近年來的熱門話題。雖然行人航位推算 (pedestrian dead reckoning, PDR)系統可以方便地實作於具備慣性傳感器的智慧型手機(smartphone) 用於室內定位，但是行人航位推算系統的誤差會迅速累積而導致精確度快速下降而造成推算的位置無法被採用。
為了解決這個問題，我們提出了合作式行人航位推算 (collaborative PDR, CPDR) 系統。CPDR的主要概念是利用使用者鄰居的位置資訊，藉由機會式卡爾曼濾波器 (opportunistic Kalman filter) 重新計算使用者的估算位置，藉以提高其精確度。另一方面，向後校正方法 (backward correction) 用於來更正行人的軌跡的精準度。為了驗證 CPDR 系統，我們在 Apple iPhone 5 建立程式並用以進行實驗。實驗結果顯示CPDR 比 PDR 達到更好的定位精準度並可實際應用於室內定位。 | zh_TW |
| dc.description.abstract | Indoor localization has become a popular topic in recent years. While self-contained pedestrian dead reckoning (PDR) systems can be conveniently implemented on a smartphone with built-in inertial sensors for indoor localization, the error of the estimated position for a PDR system can accumulate quickly and results in an unacceptable position accuracy. To address this issue, we propose the collaborative pedestrian dead reckoning (CPDR) system. The main idea of the CPDR system is when users are near to each other, we can leverage the proximity information to improve their estimated positions by means of the opportunistic Kalman filter. In addition, the backward correction scheme is used to improve the accuracy of user’s trajectory. To evaluate the CPDR system, a prototype is implemented on Apple’s iPhone 5. The experiment results show that the CPDR system achieves a better position accuracy than the raw PDR system. | en_US |
| DC.subject | 室內定位 | zh_TW |
| DC.subject | 航位推算 | zh_TW |
| DC.subject | 卡爾曼濾波器 | zh_TW |
| DC.subject | Indoor Localization | en_US |
| DC.subject | Dead Reckoning | en_US |
| DC.subject | Kalman filter | en_US |
| DC.title | An Indoor Collaborative Pedestrian Dead Reckoning System | en_US |
| dc.language.iso | en_US | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |