隨著資通訊技術快速的發展，各式多元化應用與智慧手持裝置服務已逐漸為世人所重視，室內位置導向服務(Indoor Location Based Services, Indoor LBS)之應用也與日俱增，使得提升室內定位準確度成為一重要研究議題。近年來，許多室內定位系統藉由使用環境中之無線射頻信號作為定位計算依據，然無線射頻信號容易受到環境干擾而降低定位準確度，因此對於定位準確度之議題上需進行研究與改良。

室內定位系統通常只採用一種訊號發射器及接收器來取得定位參考點的資料，傳統之定位系統使用最近鄰居法(K-Nearest Neighbors, KNN)或多點定位法(Multilateration)做為定位演算方法，因此造成定位準確度受限於單一參考點的資訊及所使用的定位演算方法而無法使準確度獲得提升。本研究之整合異質網路(Integrated Heterogeneous Networks, IHN)室內定位系統，藉由採用低功耗之藍牙發射器(Beacon)獲取額外的定位訊號，並且本研究透過結合指紋比對法(Fingerprinting)以修正多點定位法與定義模糊區域，以提升子區域選擇的準確度，最後提出門檻值機制，用以選取訊號發射器，達到提升目前室內定位系統的準確度。

根據本研究之效能分析結果顯示，整合異質網路室內定位系統所求得之平均誤差距離為1.21公尺，其優於使用單一訊號發射器(Wi-Fi AP)的混合演算法平均誤差距離1.29公尺與單一訊號發射器(Beacon)的混合演算法平均誤差距離1.33公尺。研究結果顯示，本研究所提出之IHN室內定位系統可有效的提升定位準確度。

Applications and services for smart handheld devices have garnered considerable attention worldwide, as have applications for Indoor Location-based Services (Indoor LBS). Improving their indoor positioning accuracy is a significant challenge. In recent years, many indoor positioning studies have used radio frequency for positioning; however, a signal’s susceptibility to environmental interference can decrease positioning accuracy. Increasing positioning accuracy is a complex problem and remains to be solved.

Indoor positioning systems typically use only a signal transmitter (Wi-Fi AP) and receiver to acquire reference point data. The systems then use the K-Nearest Neighbor algorithm (KNN) or a Multilateration algorithm for positioning. Consequently, positioning accuracy is limited by both the positioning algorithm and the use of a single source for acquisition of reference point data. This study utilizes the novel Integrated Heterogeneous Networks Indoor Positioning System (IHNIPS) with low-energy Bluetooth Beacon to obtain positioning data. A fingerprint algorithm is then applied to revise the multilateration algorithm and define the fuzzy area to improve the accuracy of subspace selection. Last, novel threshold mechanisms are used to choose the transmitter for positioning and thereby improve current positioning accuracy.

Experimental results show that the average error distance is 1.29m for Wi-Fi AP and 1.33m for Beacon. And the average error distance is 1.21m in the proposed Integrated Heterogeneous Networks Indoor Positioning System. The IHNIPS outperforms both Wi-Fi AP and Beacon. Thus, positioning accuracy is improved.

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