近年智慧行動載具數量呈指數性成長，網路技術與應用服務需求越來越多元，其中適地性服務(location based service, LBS)已成為廣泛探討之應用。而定位技術是發展LBS的重要關鍵，定位準確度越高越能提供適當的相關資訊，促使更多的應用情境產生。因此，如何提升定位準確度始終是備受矚目的議題。
本研究提出基於可見光通訊(visible light communication, VLC)之室內定位機制，修改發光二極體(light-emitting diode, LED)電路作為訊號傳送端且安裝於天花板作為通訊與照明二用。以智慧型行動裝置作為接收端，負責接收VLC訊號影像，透過影像處理辨別各傳送端之頻率訊號來取得座標資訊，並利用本研究提出之室內定位機制計算出目標裝置的位置。本研究有別以往傳統VLC定位研究使用接收信號角度定位法(angle of arrival, AOA)作為定位機制，提出基於智慧型移動裝置之指紋(Fingerprinting)定位機制，並進一步改良基於Fingerprinting之k個最近鄰居(k-nearest neighbors, k-NN)定位機制，提出動態k-NN (dynamic k-NN, DKNN)機制。此外，本研究針對三維空間定位提出AOA與Fingerprinting混合定位機制，減少Fingerprinting在訓練階段所需時間，並且有效提升室內定位系統的準確度。
本研究以兩種維度進行定位探討，分別為高度固定的二維定位與高度可變的三維定位。在高度不變的情境中，本研究提出DKNN之Fingerprinting定位機制，其平均誤差距離為0.15公分。此外，亦針對傳統AOA以及傳統k-NN之Fingerprinting定位機制進行比較，AOA平均誤差為11.43公分；傳統k-NN平均誤差為1.47公分。在高度可變的情境中，對AOA結合Fingerprinting之混合定位機制、AOA與一般Fingerprinting進行比較。在不考慮高度誤差時，AOA與Fingerprinting混合機制之平均誤差為2.43公分；AOA為11.10公分；一般Fingerprinting為2.74公分。考慮高度誤差時，AOA與Fingerprinting混合定位機制之平均誤差為2.83公分；AOA為11.62公分；一般Fingerprinting為4.15公分。由實驗結果得知，本研究提出之定位機制可提供較高之定位準確度。

The explosive growth of smart mobile devices, network technologies and application services is intensifying. The most well-known service is location-based service (LBS) and the positioning technology is an important key to the advancement of LBSs. More accurate positioning provides appropriate information and can be applied to more application scenarios. Therefore, how to improve the positioning accuracy has always been a subject of study.
This study proposes a visible light communication (VLC) based indoor positioning mechanism. Modifications were made on a light-emitting diode (LED) circuit and LEDs with the modified circuits were mounted on the ceiling for transmitting VLC signals. A smart mobile device was used as a receiver to receive the information from the VLC transmitters. The information and frequency signals from each VLC transmitter were analyzed via image processing. The position of the target device was calculated using the proposed indoor positioning mechanism. Unlike previous studies on VLC positioning, which used the angle of arrival (AOA) technique as their positioning mechanism, this study proposes a fingerprinting positioning mechanism based on a smart mobile device instead. Furthermore, improvement was made on a k-nearest neighbor (k-NN) positioning mechanism based on fingerprinting, called dynamic k-NN (DKNN) mechanism. Additionally, this study proposes an AOA and fingerprinting fusion positioning mechanism for three-dimensional (3D) positioning, which reduces the time required for fingerprinting training and effectively improves the accuracy of indoor positioning system.
The study focuses on two different types of dimensions positioning, i.e. fixed height of 2D positioning and changeable height of 3D positioning. For the fixed height of 2D positioning, this study proposes a DKNN fingerprinting positioning mechanism with an average error distance of 0.15 cm. In addition, traditional AOA and typical k-NN fingerprinting positioning mechanisms were included for comparison. The average error distance was 11.43 cm for the traditional AOA and 1.47 cm for the typical k-NN. For the changeable height 3D positioning, AOA and fingerprinting fusion positioning mechanism in the system was compared with the traditional AOA mechanism and pure fingerprinting. When the height error was not taken into account, the average error was 2.43 cm for AOA and fingerprinting fusion positioning mechanisms, 11.10 cm for the traditional AOA and 2.74 cm for pure fingerprinting. When the height error was considered, the average error distance was of AOA and fingerprinting fusion positioning mechanism is 2.83 cm for AOA and fingerprinting fusion positioning mechanism, 11.62 cm for the traditional AOA and 4.15 cm for pure fingerprinting. The experimental results confirm that the proposed mechanism can provide higher positioning accuracy.

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