為迎合未來物聯網(internet of thing, IoT)智慧化時代，資通訊技術串聯了許多感測裝置來提供更多樣性之服務，亦促成室內適地性服務(indoor location based service, Indoor LBS)的快速發展，其中室內定位為Indoor LBS之重要基礎。目前常見之室內定位方法以RFID、藍牙、地磁或Wi-Fi為主，其定位精準為公尺等級。為使定位系統可以提供更多元服務以符合更多應用情境，關鍵在於精準度之等級，如何有效降低平均錯誤距離並提高精準度儼然成為室內定位之關注議題。

基於可見光通訊(visible light communication, VLC)技術之室內定位解決方案，不僅可使平均錯誤距離降低為公寸等級，也能克服基於射頻(radio frequency, RF)技術因信號浮動而降低定位準確度之問題。本研究提出一創新VLC室內定位系統，使用發光二極體(light-emitting diode, LED)為信號傳送端並佈署於室內，目標接收端以智慧感測器來負責接收LED之頻率、色溫與輝度。先透過辨別LED之頻率來選擇定位子區域，以縮小定位範圍，並以混合式指紋比較法(Fingerprint)來運算目標物之位置。有別於一般Fingerprint使用RSSI做為特徵參數，容易受信號折射或繞射之干擾，本研究以光之直線性作為定位基礎，提供更高精準度之室內定位系統。

本研究之混合式演算機制，其平均錯誤距離為2.55公分，最大錯誤距離為32.25公分，定位精準度為99.87%。本研究亦針對另外五種方法進行比較：使用歐基里德距離(Euclidean distance)、曼哈頓距離(Manhattan distance)、基於子區域之歐基里德距離、基於子區域之曼哈頓距離以及基於子區域之權重機制。歐基里德距離之平均錯誤距離為14.65公分，最大錯誤距離為114.26公分，定位精準度為95.3%；曼哈頓距離法之平均錯誤距離為14.06公，最大錯誤距離為116.29公分，定位精準度為95.7%；基於子區域之歐基里德距離之平均錯誤距離為2.95公分，最大錯誤距離為33.67公分，定位精準度為99.83%；基於子區域之曼哈頓距離其平均錯誤距離為2.73公分，最大錯誤距離為32.81公分，定位精準度為99.85%；基於子區域之權重機制其平均錯誤距離為2.78公分，最大錯誤距離為34.06公分，定位精準度為99.85%。因此本研究之演算機制不僅降低平均錯誤距離82.59%，且有效降低最大錯誤距離71.77%，整體定位系統之精準度提升4.57%。

To have a generation for internet of things, technologies combine sensors for providing multiple services. It makes indoor location based service (LBS) develop rapidly. Indoor positioning is the important base of location-based and widely discussed and applied. Many radio frequency technologies have been developed for indoor positioning systems for multiple scenarios. Common indoor positioning technologies are RFID, Bluetooth, geomagnetic and Wi-Fi etc. The average error distances in these technologies are at the meter level. With the need for more accurate positioning, how to reduce average and maximum error distances and increase accuracy is the key issue in indoor positioning systems.

The indoor positioning solution based on visible light communication (VLC) not only can reduce the average error distance to the decimeter level, but also solve the instability problem of RSSI values based on radio frequency. This study proposes a novel indoor positioning system with a hybrid algorithm. In the proposed system, the LEDs are signal transmitters and deployed on a ceiling. The target receiver is a smart sensor and responsible for receiving the frequency, color temperature and luminance of the lights. First, the system identifies the frequency of LEDs to select the subspace that narrows down the positioning area and compute the position of the target based on hybrid fingerprint. The proposed fingerprint is different from the typical fingerprint that is based on RSSI values. RSSI signals are easily disturbed by refraction or diffraction. This research is based on line-of-sight light emission, which provides higher accuracy for indoor positioning.

In the proposed system, the average and maximum error distances and the accuracy are 2.55 cm, 32.25 cm and 99.87%. This study also compared the hybrid fingerprint with five types of fingerprints, i.e. fingerprint with Euclidean distance, fingerprint with Manhattan distance, subspace-based fingerprint with Euclidean distance, fingerprint with Manhattan distance, fingerprint with weighting mechanism and fingerprint with hybrid mechanism. The average and maximum error distances and accuracy were 14.65 cm, 114.26 cm and 95.3% for fingerprint with Euclidean distance; 14.06 cm, 116.29 cm and 95.7% for fingerprint with Manhattan distance; 2.95 cm, 33.67 cm and 99.83% for the subspace-based fingerprint with Euclidean distance; 2.73 cm, 32.81 cm and 99.85% for the subspace-based fingerprint with Manhattan distance; and 2.78 cm, 34.06 cm and 99.85% for the subspace-base fingerprint with weighting mechanism. The max error distance was effectively reduced by the subspace selection. The proposed system reduced the average and maximum error distances by 82.59% and 71.77% and increased the accuracy by 4.57%.

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