隨著無線通訊與微電子技術的快速發展，與其有關之各式多元化應用與服務，則日漸為世人所重視，使得室內位置導向服務(Indoor Location Based Services, Indoor LBS)應用也日益劇增，因而也使得研究如何提昇室內定位精準度成為相當熱門之研究題目。近年來，雖然在室內定位方面已使用了各種的技術，來克服室內定位之各種干擾因素，但對於其定位精準度與定位計算複雜度之議題上仍有許多問題尚待解決。

目前室內定位系統通常只採用接收器或發送器來取得定位參考點的資料，以及使用最近鄰居法(K-Nearest Neighbors, KNN)或三角定位法(Trilateration)做為定位演算方法，而造成定位精準度受限於單一種參考點的資料以及所使用的定位演算方法而無法再提昇。所以本研究提出一個採用接收器和發送器取得定位參考點的資料，以及六種定位演算方法的Novel Fingerprinting Mechanisms(NFM)室內定位系統，以改善目前室內定位系統的精準度。

根據本研究之實作結果顯示，NFM室內定位系統所求得之平均誤差距離為1.18公尺，這比最近鄰居法的平均誤差距離1.35公尺與三角定位法的平均誤差距離2.23公尺都要好，這證實本研究已提昇了室內定位精準度。

With the rapid development of wireless communications and microelectronic technology, a wide range of diverse applications and services based on smart handheld devices have increasingly drawn the attention from all over the world, and the popularity of Indoor Location Based Services (Indoor LBS) applications has also gradually increased. Therefore, how to improve indoor positioning accuracy becomes a very important issue. In recent years, although indoor positioning has been carried out using a variety of techniques, the problem of the computational complexity of ensuring positioning accuracy and positioning is yet to be solved.

Currently, the indoor positioning system usually utilizes only the receiver or the transmitter to obtain the reference point’s data, and uses only the K-Nearest Neighbors (KNN) or Trilateration algorithm to carry out the positioning. As a result, the positioning accuracy is limited by single source of the reference point’s data as well as the positioning algorithm used. This study, however, proposes a Novel Fingerprinting Mechanisms (NFM) indoor positioning system, which uses both the receiver and transmitter to obtain positioning data and employs six positioning mechanisms to improve the current positioning accuracy.

According to our experiment, the results show that the average error distance is 1.18 m in the NFM indoor positioning system. It outperforms both KNN and Trilateration systems, in which the average error distance is 1.35 m for KNN, and 2.23 m for Trilateration. This study proves that the positioning accuracy is actually improved.

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