| DC 欄位 |
值 |
語言 |
| DC.contributor | 資訊工程學系 | zh_TW |
| DC.creator | 陳敬忠 | zh_TW |
| DC.creator | Ching-Chung Chen | en_US |
| dc.date.accessioned | 2016-7-27T07:39:07Z | |
| dc.date.available | 2016-7-27T07:39:07Z | |
| dc.date.issued | 2016 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=103522057 | |
| 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 | 室內定位已經成為近年來很熱門的議題。大部分這些室內定位的方法仰賴於基礎設施的可用性以及額外的技術培訓，而傳統的行人航位推算(pedestrian dead reckoning, PDR)系統，可以無需額外的設施成本或是技術訓練，即可在移動設備上實驗。然而，由於PDR 系統的誤差會迅速累積，導致系統運行一小段時間就會產生不合格的位置結果。為了解決這個問題，我們提出基於超聲波的合作式行人航位推算 (ultrasound-based collaborative PDR,UCPDR)系統。
UCPDR 系統的主要想法是利用兩個步驟來降低位置座標的估計誤差值：
第一，UCPDR 會運用其他相鄰使用者使用PDR 系統所取得的座標值，以及透過兩人交換超聲波信號所得到的距離資訊，來估算出行人的座標位置。
第二，透過機會式卡爾曼濾波(opportunistic Kalman filter)使用先前的估計位置做為新的測量值來降低估計座標的誤差。除此之外，反向校正(backward correction)系統在UCPDR 系統中被用來改善用戶軌跡的精度。
為了評估UCPDR 的可行性，我們將原型建立於iOS (iPad 操作系統)平台上，而經過多次實驗後，結果顯示，透過鄰居們的位置資訊以及超聲波距離的量測進行位置校正，UCPDR 可以在運行很長一段時間後，定位所造成的誤差仍然可以限制在2 公尺以內 (當步數小於80)。 實驗顯示UCPDR 系統比傳統的
PDR 系統達到更好的定位精度並可實際應用於室內定位。 | zh_TW |
| dc.description.abstract | Indoor localization has become a popular issue in recent years. Most of the indoor localization approaches either require the availability of an infrastructure or the additional training e orts. The traditional pedestrian dead reckoning (PDR) system can be implemented on mobile devices without additional cost and training.
However, it accumulates errors quickly and leads to unacceptable results after a short period of time. To address this issue, we propose the ultrasound-based collaborative pedestrian dead reckoning (UCPDR) system. The main idea of UCPDR is to reduce the estimation error of a pedestrian′s location by two steps: First,UCPDR estimates the pedestrian′s location based on another nearby pedestrian′s location information obtained by PDR and the distance information obtained by ultrasound signals exchanged by the two pedestrians; second, reduce the error estimation through the opportunistic Kalman lter by using the previous location estimation as a new measurement. In addition, the backward correction scheme is used to improve the accuracy of user′s trajectory. To evaluate feasibility of UCPDR, a prototype is built on the iOS (iPhone Operating System) platform. The conducted
experiment results show that UCPDR is able to limit the localization error within 2m (when number of steps less than 80) after a long period of time through the help of neighbors location information and ultrasound distance information. Our UCPDR system always achieves a better localization accuracy than the traditional PDR system. | en_US |
| DC.subject | 室內定位 | zh_TW |
| DC.subject | 行人航位推算 | zh_TW |
| DC.subject | 卡爾曼濾波 | zh_TW |
| DC.subject | Indoor localization | en_US |
| DC.subject | pedestrian dead reckoning | en_US |
| DC.subject | Kalman filter | en_US |
| DC.title | Ultrasound-based 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 |