日新月異的科技世代，於可攜式裝置近年來呈現爆炸性成長，隨可攜式裝置與導航等多種技術整合的適地性服務(Location-Based Service, LBS)，其應用與日俱增。LBS中定位為重要之一環亦發展之關鍵，其定位精度越高提供系統準確位置及相關資訊，則促使提供應用面廣度提升，然於室內定位容易受環境因素之干擾，何能提升其定位精度技術始終是矚目之議題。
　　本研究提出室內定位之演算機制，其研究使用低功率藍牙(Bluetooth Low Energy, BLE)的Beacon於室內中佈署，並以一目標物(智慧型手持裝置)負責偵測收集Beacon之硬體資訊與訊號值。透過大量之訊號辦別其目標物所在區域，經系統分析訊號以過濾參考點及劃分一般以及特殊權重，利用篩選、門檻、權重模組來運算目標物偵測訊號資訊，以環境條件提供準確之室內定位機制。本研究提出三項室內定位之演算機制，系統將透過目標物(智慧型手持裝置)所偵測收集Beacon之硬體資訊與訊號值進行分析，首先藉由篩選機制將參考價值低之參考點不予進行參考運算；第二項，將參考點以門檻值檢測是否達標以劃分一般以及特殊權重，若為達標則屬特殊權重；最後，系統依一般或特殊權重進行參考點之加權運算。
　　系統基於K-近鄰算法之指紋定位，藉由本研究之演算機制，其平均誤差距離為0.711公尺，定位準確度為96.04 %，本研究亦針對常見之KNN(K-近鄰算法)、DKNN(動態K-近鄰算法)以及DKNN-W(混合式動態K-近鄰算法與權重演算法)、KNN-W(混合式K-近鄰算法與權重演算法)進行比對，KNN演算法其平均誤差距離為0.83公尺，定位準確度為94.6 %；DKNN演算法其平均誤差距離為0.796公尺，定位準確度為95.03 %；DKNN-W混合式演算法其平均誤差距離為0.779公尺，定位準確度為95.24 %；KNN-W演算法其平均誤差距離為0.729公尺，定位準確度為95.83 %。由實驗結果得知，本研究之定位演算機制可提供較高之定位準確度。

Technology and science are advancing daily. In recent years, a large amount of mobile devices result in an increase usage of the application of location-based services (LBS). The Positioning technology is incorporated into the LBS applications, including mobile device and navigation, and so on. Accordingly, the positioning technology is important for the LBS development. The indoor location accuracy is affected by the signal interference problems related to the environmental factors. How to improve indoor location accuracy is always a crucial topic.
This study examines the indoor positioning mechanism and deploys Bluetooth Low Energy (BLE) in the environment. Target’s device collects the RSSI that is transmitted by Beacon, and the system calculates the location of target by the RSSI data. Through the filter, threshold, weight module, the system filters the reference points before dividing the RSSI data into two main categories-general weighting and adaptive weighting. The system provides accurate positioning in the environment.
This study proposes three mechanisms for indoor positioning system. The system assays the RSSI data collected by target’s device. First, the RSSI data filters the good reference points. Second, the good reference points are on the threshold of a breakthrough in the special case. In the special case, the RSSI data calculates the weight by the special weight mechanism. Last, the system calculates the location of target by the weight mechanism. The study used four cases to prove the DKNN-AW is better than KNN (k-Nearest Neighbors algorithm), KNN-W (KNN with Weight algorithm), DKNN (Dynamic KNN algorithm) and DKNN-W (Dynamic KNN with Weight algorithm) mechanisms. The DKNN-AW result of average error distance is 0.711 meters, and the result of average positioning accuracy is 96.04 %. The KNN result of average error distance is 0.83 meters, and the result of average positioning accuracy is 94.6 %. The KNN -W result of average error distance is 0.729 meters, and the result of average positioning accuracy is 95.83 %. The DKNN result of average error distance is 0.796 meters, and the result of average positioning accuracy is 95.03 %. The DKNN-W result of average error distance is 0.779 meters, and the result of average positioning accuracy is 95.24 %. The study’s DKNN-AW provides high positioning accuracy, which is shown in the results.

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