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Title: 基於低功耗藍芽實現強健型室內定位
BLE-Based Implementation for Robust Indoor Localization
Authors: 許陳鑑
Hsu, Chen-Chien
Wang, Wei-Yen
Tsai, Tsung-Yu
Keywords: 低功耗藍芽
Bluetooth Low Energy
Indoor Localization
Fuzzy Inference System
Affinity Propagation Clustering Algorithm
Issue Date: 2019
Abstract: 本論文主要針對低功耗藍芽(Bluetooth Low Energy, BLE)室內定位演算法做改良,以降低誤差對室內定位結果的影響及增加準確率。本論文首先以BLE裝置佈置一無線網路環境,透過訊號強度的採集,進行演算法的計算,進而求出待測物的定位點。為改善不穩定的訊號強度對計算定位點造成的擾動,本論文採用模糊邏輯的概念,降低不穩定訊號對定位演算法的影響,並藉由近鄰傳播聚類演算法進行資料分群,計算出在模糊系統中的模糊集合,最後透過路徑圖表法由前一時刻的定位點來輔助演算法的計算,以增加定位的準確率。演算法主要分為離線與在線兩階段,離線階段係透過收集大量的資料,經過分群演算法後得到不同的群集,進而用來建置模糊規則庫;在線階段為接收即時的資料,透過模糊推論以及路徑圖表法估測出定位點。最後,本論文將對所提出的演算法進行不同情境下的實驗,並對這些實驗結果做分析。
This thesis mainly focuses on improving the Bluetooth Low Energy (BLE) based indoor localization algorithm to reduce the error of localization and increase the ac-curacy. BLE devices are used to deploy a wireless network environment. By collect-ing a set of the received signal strength, the algorithm is used to obtain the localiza-tion of the object. In order to avoid the fluctuations caused by unstable received sig-nal strength, fuzzy system is used in the proposed algorithm consist-ing of two phases: the offline phase and the online phase. In the offline phase, through collecting a large amount of data, different clusters are obtained based on a clustering algorithm. Next, the clusters are used to establish a fuzzy rule base. In the online phase, according to real-time data, fuzzy system and path graph method are used to obtain the localiza-tion result. Finally, extensive experiments are con-ducted to validate the performance of the proposed algorithm in various situations.
Other Identifiers: G060575016H
Appears in Collections:學位論文

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