近年來家庭自動化程式推陳出新，若是要提供好的自動化服務則需要精確找出使用者位置的能力，室內定位也是最近熱門的研究主題對於在室內環境中自動地提供數位居家照護服務給使用者。然而室內定位由於在室內無法使用常見的全球位置測定系統 (Global Positioning System, GPS)，最近幾年無線感測網路 (Wireless Sensor Networks)的蓬勃發展，加上無線感測網路的性質正好適合實作室內定位，延伸出根據無線感測網路的物理特性進而實作室內定位的眾多相關討論議題。在本論文中，我們討論接收訊號強度 (Received Signal Strength Indication,RSSI)，並且提出能讓使用者處於室內環境接近某個參考節點時能正確判斷的CTA(Closer Tracking Algorithm)混和風格方法，在我們所提出的CTA 方法藉由事先定義的門檻值設計成切換ACA (Approximately Closer Approach)與RTT (Real-time Tracking)兩種模式，藉由切換兩種模式來達到適應更多定位情形的發生，此外也提出移動參考節點來減少沒有達到我們事先定義的門檻值內的情形以增加定位的準確性。我們實驗採用IEEE 802.15.4 協定的CC2431 晶片ZigBee 模組實作我們的方法以及比較其它方法(如Fingerprinting、Real-Time Tracking 方法)，從實驗結果我們可以驗證我們提出的CTA 方法在不同情況下挑選了適合的模式並且達到了增加定位的準確性以及證實定位結果有非常高的可信度，此外我們透過改善參考節點的擺設以及移動參考節點搭配上我們所提出的方法讓我們的定位準確度更加準確。實驗結果表示我們的方法準確率可以達到誤差值在一公尺以內，以及在距離參考節點一公尺範圍內達到90%的精確性。

For the various applications in home automation, the service system requires to precisely estimate user’s locations by certain sensors. It is considered as a challenge to automatically serve a mobile user in an indoor environment. However, indoor localization cannot be carried out effectively by the well-know Global Positioning System (GPS). In recent years, Wireless Sensor Networks (WSNs) are thus popularly used to locate a mobile object in an indoor environment. Some physical features are widely discussed to solve indoor localization in WSNs. In this paper, we inquired about the RSSI-based solutions for indoor localization, and proposed a new hybrid-styled Closer Tracking Algorithm (CTA) to locate a mobile user in an indoor environment. Under the proposed CTA, a mode-changed operation was designed to automatically switch the Approximately Closer Approach (ACA) and the Real-time Tracking (RTT) methods according to the pre-defined thresholds, which we had tuned. At the same time, we designed the movable reference nodes to reduce the uncovered ranges of the RTT part for increasing the accuracy. The proposed CTA was evaluated by using ZigBee CC2431 modules. In the experimental results, the CTA can properly select an adaptive mode to improve the localization accuracy with high confidence. Furthermore, the accuracy can be improved by the deployment and movement of the reference nodes. The results showed that the proposed CTA can accurately determine the position with error distance less than 1 meter. At the same time, the CTA has at least 90% precision when the distance is less than one meter.

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