| DC 欄位 |
值 |
語言 |
| DC.contributor | 資訊工程學系 | zh_TW |
| DC.creator | 李重儀 | zh_TW |
| DC.creator | Chong-Yi Li | en_US |
| dc.date.accessioned | 2006-7-22T07:39:07Z | |
| dc.date.available | 2006-7-22T07:39:07Z | |
| dc.date.issued | 2006 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=93522011 | |
| 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 | 近期有關位置感知計算的研究領域逐漸受到重視，許多商業產品、大眾公共設施、緊急危難救助及軍事設備都有實際上的應用，例如行車導航、旅遊景點介紹及個人安全維護等。現今有許多不同方法來達成偵測使用者的空間位置，利用目前普遍且成熟的全球定位系統，能夠讓使用者知道所處的戶外地理位置。由於全球定位系統在室內環境中會受到建築物屏障效應的影響，使得全球定位系統無法有效的應用在室內環境定位中，因此許多研究者紛紛利用感測網路或普遍的無線網路架構來實做室內定位研究。本篇論文著重在如何建構一套低成本與維護方便的室內定位系統，在室內環境複雜的情況下，我們能容易取得基地台間的訊號強度訊號，所以本篇論文提出兩種基於現今無線網路架構中的室內定位方法。第一種方法是以放射性基底函數網路（radial-basis-function network）為基礎，而另一種方式是以統計的方式為基礎，上述兩種方式都有各自的優點及限制。最後對於我們所提出的兩種室內定位方法，利用生活環境實際資料及一個具有指標意義的資料集進行測試並評估其效果好壞。 | zh_TW |
| dc.description.abstract | Location-aware Computing is a recent interesting research field and has many practical applications in commercial, civil, emergency, and military environments. Examples include navigation, tourism, and personal security, etc. Many different approaches have been proposed to tackle the problem of determining the user position. In an outdoor environment, the Global Positioning System （GPS）is the most population approach. However, due to the poor indoor coverage, the GPS cannot provide a satisfactory solution to the problem of indoor location estimation. Therefore, many approaches to indoor location estimation rely on dedicated sensor networks and/or existing wireless LAN infrastructures. Based on the factors of cost and maintenance complexity, this thesis focuses on the latter approach. In many situations, the only information that is available is the received signal strength indication（RSSI）value. Therefore, this thesis proposes two approaches to indoor location estimation based on existing wireless LAN infrastructures. While our approach is based on the use of a radial-basis-function（RBF） network, the other approach is a statistical model. Each approach has its own advantages and limitations. The performance of these two proposed approaches is demonstrated by tests performed in a real-world environment and one benchmark data set. | en_US |
| DC.subject | 室內定位 | zh_TW |
| DC.subject | indoor location estimation | en_US |
| DC.title | 室內定位之研製與實作 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | The Implementation of an Indoor Geolocation System | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |