Wi-Fi室內定位使用粒子群演算法

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姓名

余宗鴻(Tsung-hung Yu) 查詢紙本館藏

畢業系所

通訊工程學系在職專班

論文名稱

Wi-Fi室內定位使用粒子群演算法
(Wi-Fi Indoor Positioning System Using Particle Swarm Optimization)

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摘要(中)

本篇論文主要利用WiFi AP的訊號強度來進行室內定位，首先透過擷取無線接取點(Wi-Fi Access Point) 內的接收訊號強度(Received Signal Strength Indicator, RSSI)來建構出離線地圖，接下來於線上定位階段正確地取得各個無線接取點(Wi-Fi Access Point)內信標框(beacon frame)的相關資訊，並將接收到的訊號強度轉換成位置資訊；利用各種適用於定位系統的演算法(K個最近鄰居演算法、K分群演算法、粒子群優化演算法)進行室內定位，透過比較這些演算法我們可以發現，本篇論文所使用的粒子群優化演算法（Particle Swarm Optimization，PSO）應用於定位系統上可以達到較高的定位精準度與收斂速度。
根據模擬實驗的結果，本文所提出的粒子群演算法，在平均定位誤差優於其它演算法，平均定位誤差在1M內，最大定位誤差在1.5M內，非常符合智慧型手持式裝置在室內定位服務上的需求。

摘要(英)

This thesis is mainly to use the Wi-Fi access points signal strength for indoor positioning. i.e. Received Signal Strength Indicator measurements from multiple Wi-Fi access points. During an offline phase, fingerprints are collected at known positions in the building. This database of locations and the associated fingerprints are called the radio map. During an online phase, the current Wi-Fi fingerprint Particle Swarm optimization are compared with those of the radio map. This paper compared different algorithm, such as K Nearest Neighbor algorithm, K-means clustering algorithms, Particle Swarm Optimization, we can find the Particle Swarm Optimization algorithm on the indoor positioning system can achieve high positioning accuracy and convergence speed.
This simulation results showed the proposed Particle Swarm Optimization algorithm, the average location error better than others, the median error of 1m, the maximum positioning error in 1.5M, it means Particle Swarm Optimization algorithm more suitable for indoor positioning and smart handheld devices.

關鍵字(中)

★ 室內定位
★ K個最近鄰居演算法
★ K分群演算法
★ 粒子群演算法

關鍵字(英)

★ Indoor positioning system
★ K Nearest Neighbor algorithm
★ K-means clustering algorithms
★ Particle Swarm Optimization

論文目次

中文摘要 I
英文摘要 II
致謝 III
目錄 IV
圖目錄 VIII
表目錄 IX
第一章緒論 01
1.1 研究背景與動機 01
1.2 研究目的 04
1.3 研究方法及內容 07
1.4 論文大綱 08
第二章現有定位技術探討 09
2.1 室內定位相關技術 09
2.1.1 無線射頻辨識 09
2.1.2 紅外線定位技術 11
2.1.3 全球定位系統 12
2.1.4 Wi-Fi定位技術 13
2.2 各個定位法相關介紹 14
2.2.1 訊號抵達時間定位法 14
2.2.2 訊號抵達時間差定位法 14
2.2.3 訊號強度定位法 15
2.2.4 訊號抵達角度定位法 16
2.3 探討現有的Wi-Fi 定位方法及技術 17
2.3.1 無線傳播模型估測法 17
2.3.2 靜態場景分析法 18
2.4 定位演算法介紹 20
2.4.1 K個最近鄰居法 20
2.4.2 K群演算法 22
2.5 Wi-Fi室內定位技術相關文獻探討 24
第三章研究內容 27
第四章研究方法與敘述 30
第五章研究法比較結果與分析 36
第六章結論與未來展望 40
參考文獻 41
作者簡歷 47
附錄A 48
附錄B 51
附錄C 57

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指導教授

林嘉慶(Jia-chin Lin)

審核日期

2015-8-19

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