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研究生:鄭宇哲
研究生(外文):Yu-Che Cheng
論文名稱:無線定位於惡劣氣候下的補償技術之進一步研究
論文名稱(外文):An Advanced Study of Compensating for Bad Weather inWireless Localization
指導教授:方士豪方士豪引用關係
指導教授(外文):Shih-Hau Fang
口試委員:錢膺仁曹昱呂道誠
口試委員(外文):Ying-Ren ChienYu TsaoDau-Cheng Lu
口試日期:2017-12-5
學位類別:碩士
校院名稱:元智大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:106
語文別:中文
論文頁數:34
中文關鍵詞:機器學習
外文關鍵詞:Machine learning
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隨著現在科技發展以及手機的普及,人手一機已成現代趨勢,同時也帶起了許多適地性服務。基於適地性服務的發起,引起了許多人投入定位相關研究,其中一項便是無線電波訊號定位。然而此項定位法卻容易受天氣因素影響,因此本論文透過機器學習的特性,結合天氣因素,將低無線定位法受到天氣造成的影響。
此篇論文提出了利用機器學習的特性,對天氣因素進行分群。並由這些天氣群集中所對應的無線電波資料,找出距離誤差最低的群集設為基底。之後利用類神經網路訓練訊號補償模型,將其他群集的訊號強度補償至基底,而後再利用路徑衰減模型估測出使用者和基地台之間的距離。最後和以前常用的方法進行比較,以驗證此篇論文的優點。
With the current development of science and technology and the popularization of mobile phones, the manpower and equipment have become a modern trend and brought many suitable services. Based on the launch of a service of locality, many people have been involved in positioning related research. One of them is the positioning of radio signals. However, this positioning method is easily influenced by the weather. Therefore, this paper uses the characteristics of machine learning and the weather to influenced the low-radio location method by the weather.

This essay proposes the use of machine learning features to group weather factors. And from these radio waves corresponding to the weather cluster data, and the cluster with the lowest distance error as the base. After that, the neural network is used to train the signal compensation model, and the signal intensities of the other clusters are compensated to the base. Then, the path attenuation model is used to estimate the distance between the user and the base station. Finally, it compares with the commonly used methods before to verify the advantages of this paper.
Contents . . . . . . . . . . . . . . . . . . . . . . . .vii
List of Tables . . . . . . . . . . . . . . . . . . . . . ix
List of Figures . . . . . . . . . . . . . . . . . . . . . x
Chapter 1. 緒論. . . . . . . . . . . . . . . . . . . . . 2
1.1 研究動機以及目的. . . . . . . . . . . . . . . . . . . 2
1.2 相關文獻. . . . . . . . . . . . . . . . . . . . . . . 3
1.3 論文架構. . . . . . . . . . . . . . . . . . . . . . . 3
Chapter 2. 理論基礎. . . . . . . . . . . . . . . . . . . . 4
2.1 K-平均聚類(K-means clustering) . . . . . . . . . . . . 4
2.2 Deep Denoising Autoencoder . . . . . . . . . . . . . 5
2.3 路徑對數衰減模型(Log-Distance Path Loss Model) . . . . 6
2.4 三角定位(Triangulation) . . . . . . . . . . . . . . . 7
2.5 決策樹(Dscision Tree) . . . . . . . . . . . . . . . . 9
Chapter 3. 系統架構與資料說明. . . . . . . . . . . . . . . 10
3.1 建構天氣資料分類器. . . . . . . . . . . . . . . . . . 11
3.2 建構訊號補償模型. . . . . . . . . . . . . . . . . . . 11
3.3 資料說明. . . . . . . . . . . . . . . . . . . . . . . 12
Chapter 4. 實驗環境及實驗設置. . . . . . . . . . . . . . . 15
4.1 無線電波訊號量測設置. . . . . . . . . . . . . . . . . 15
4.2 天氣資料量測設置. . . . . . . . . . . . . . . . . . . 16
4.3 量測環境設置. . . . . . . . . . . . . . . . . . . . . 20
Chapter 5. 實驗結果. . . . . . . . . . . . . . . . . . . 22
5.1 天氣資料分群數分析. . . . . . . . . . . . . . . . . . 22
5.2 天氣群集分類. . . . . . . . . . . . . . . . . . . . . 23
5.3 距離估測誤差. . . . . . . . . . . . . . . . . . . . . 23
Chapter 6. 結論與未來展望. . . . . . . . . . . . . . . . 30
Bibliography . . . . . . . . . . . . . . . . . . . . . . 31
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