基於低功耗藍芽實現強健型室內定位

BLE-Based Implementation for Robust Indoor Localization

蔡宗佑(Tsai Tsung-Yu)

指導教授 ： 許陳鑑 ； 王偉彥

國立臺灣師範大學/科技與工程學院/電機工程學系/碩士(2019年)

https://doi.org/10.6345/NTNU201900972

摘要

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。

關鍵字

低功耗藍芽 ； 室內定位 ； 模糊推論系統 ； 近鄰傳播聚類演算法

並列摘要

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.

並列關鍵字

Bluetooth Low Energy ； Indoor Localization ； Fuzzy Inference System ； Affinity Propagation Clustering Algorithm

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