博碩士論文 101523040 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:11 、訪客IP:3.233.224.8
姓名 許晏銘(Yen-ming Hsu)  查詢紙本館藏   畢業系所 通訊工程學系
論文名稱 利用虛擬指紋建置法之智慧型天線系統實現精準室內定位技術
(Indoor Location Estimation Using Smart Antenna System with Virtual Fingerprint Construction Scheme)
相關論文
★ 利用智慧天線系統實現精準室內定位技術★ 電力線通訊之競爭存取與路由方法設計與實現
★ 設計與實作基於GRAPES函式庫之P2P即時串流系統★ 利用離散餘弦基礎之聲音浮水印達到室內定位技術
★ 即時影像串流自適應播放系統之研究★ 利用模糊邏輯控制器於蜂巢式網路降低位置管理機制成本
★ 基於支持向量機及模糊推理之地震預警系統研製★ 基於行動裝置之分散式多人會議系統
★ 以分群為基礎之3D無線與光學網路晶片頻道存取方法★ 基於收前先聽LBR機制之授權型輔助接入LAA架構下於異質網路中暴露節點之研究
★ 支援跳頻之IEEE 802.15.4 ZigBee無線隨身網路機制設計與實現★ 應用於IEEE 802.16行動無線都會網路省電模式參數設定之智慧策略
★ IEEE 802.15.4 ZigBee 無線隨身網路高效能路由演算法分析與設計★ 應用於IEEE 802.16無線寬頻都會網路之具調適性自動重傳請求回報機制
★ 無線感測網路為基礎之空間平面圖自動建構之技術★ 隨機指定埠號對稱式網址轉換器穿透之研究
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 ( 永不開放)
摘要(中) 隨著無線通訊科技迅速發展,無線區域網路 (Wireless LAN, WLAN) 的終端裝置及無線接取點 (Access Point, AP) 裝置的普及化,WLAN技術已廣泛應用在商務區、學校、機場及其它公共區域,以定位為基礎的服務 (Location-Based Service, LBS) 在商業、公共安全的應用也漸漸地受到重視。室內定位技術之相關應用如:醫療照顧、人員追蹤、緊急救難系統及商場動線導引等,被廣泛提出並予以討論。
關於室內定位的估測,許多智慧型定位技術早已被提出,並且可分為幾類。而特徵指紋辨識的定位方法是其中之一,其特點是在離線階段 (offline phase) 建立室內空間的無線訊號參考點 ((reference points, RPs)) 資料庫,並在上線階段 (online phase) 利用無線訊號RP資料庫來估算位置。然而,建置室內無線訊號資料庫是最關鍵的步驟,其中估測位置的準確度與RP的分佈和數量息息相關。一般情況下,收集接收的訊號強度((received signal strengths, RSSs) )所需要的時間與“真實的RP”之數量成正比。為減少離線階段整體過程的複雜性,本文提出一種有效率的改進方法,利用已知的訊號傳播模型,在RP的資料庫中,建構出“虛擬”的RP。利用RSS透過訊號傳播模型所計算出的虛擬RP,替代離線階段所收集的“真實”的RP 。本論文提出的方法,不僅可以縮短離線階段的整體收集時間,更能實現高精準度的室內定位成果。使用智慧型天線系統((Smart Antenna System, SAS) )的室內定位估測相較於一般的無線接取點 (access point)需要較長的時間,因為SAS經由多個天線組合來收集所有的RSS。透過本文提出的虛擬指紋建置法結合SAS,可以很容易地獲得足夠和有效的RSS,以更有效率的方式建置室內無線訊號的資料庫。實驗結果說明,運作於SAS上的虛擬指紋建置法可於室內無線訊號資料庫中減少33%“真實”的RP數量,並且達到高精準度的定位結果。
摘要(英) Regarding indoor location estimation, many smart positioning techniques have been proposed and they could be classified into several categories. The fingerprinting is one of categories and its features are to build the indoor radio map during offline phase and to utilize the radio map to estimate the location during online phase. Creating indoor radio map is the most critical step, where the accuracy of location estimation depends on the distribution and number of reference points (RPs). Usually, the time required to collect received signal strengths (RSSs) is proportional to the number of ’real’ RPs. To reduce the complexity of offline process, this paper proposes an efficient scheme, which is based on the well-known signal propagation model, to construct ’virtual’ RPs in indoor radio map. The RSSs on virtual RPs are calculated and used to substitute for the training RSSs on ’real’ RPs collected during offline phase. The proposed scheme can not only shorten the collecting time but also achieve high accuracy for indoor positioning. The indoor location estimation using smart antenna system (SAS) also requires longer time to collect all RSS information because of multiple antennas. Combining this scheme with SAS, it can easily obtain enough and valid RSSs information to build the indoor radio map in a more efficient way. Experimental results showed that applying virtual fingerprint construction scheme on SAS can decrease 33% ’real’ RPs in indoor radio map without scarifying the positioning accuracy.
關鍵字(中) ★ 區域特徵指紋辨識法
★ 室內定位
★ 智慧型天線系統
★ 位置評估
★ 訊號強度
★ 無線區域網路
關鍵字(英) ★ Fingerprint
★ Indoor Positioning
★ Smart Antenna System
★ Location Estimation
★ Receive Signal Strength
★ WLAN
論文目次 中文摘要 v
ABSTRACT vi
CONTENTS vii
LIST OF FIGURES ix
1. INTRODUCTION 1
2. RELATED WORKS 3
2-1 Smart Antenna System 3
2-2 Indoor Location Method 4
2-2-1 Time of Arrival 5
2-2-2 Angle of Arrival 6
2-2-3 Location Fingerprinting 7
2-2-4 Received Signal Strength 8
3. PROPOSED INDOOR LOCATION SYSTEM 9
3-1 Smart Antenna System 11
3-2 Smart Indoor Location Estimation 18
3-2-1 Location Estimation Algorithm 18
3-3 Probabilistic-Based Estimation 19
3-3-1 Scheme of Virtual Fingerprint Construction 19
3-3-2 The Algorithm of Antenna Set Selection 21
3-3-3 Online Location Estimation 23
4. EXPERIMENTAL RESULTS AND ANALYSIS 25
4-1 Experimental Environment 25
4-2 Experimental Results 27
5. CONCLUSIONS 31
6. ACKNOWLEDGMENTS 31
7. REFERENCES 32
參考文獻 [1] P. Bahl and V. N. Padmanabhan, “RADAR: An In-Building RF-BASED User Location and Tracking System,” Proceedings of INFOCOM, vol. 2, pp. 775–784, April 2000.
[2] A. LaMarca, Y. Chawathe, S. Consolvo, J. Hightower, I. E. Smith, J. Scott, T. Sohn, J. Howard, J. Hughes, F. Potter, J. Tabert, P. Powledge, G. Borriello, and B. N. Schilit, “Place Lab: Device Positioning Using Radio Beacons in the Wild.” Proceedings of International Conference on Pervasive Computing, pp. 116–133, 2005.
[3] P. Prasithsangaree, P. Krishnamurthy, and P. K. Chrysanthis, “On Indoor Position Location with Wireless LANs,” Proceedings of IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC’02), Lisbon, Portugal, Sep. 2002.
[4] R. Battiti, M. Brunato, and A. Villani, “Statistical Learning Theory for Location Fingerprinting in Wireless LANs,” Technical Report, Oct. 2002. [Online]. Available: http://rtm.science.unitn.it/ battiti/ archive/86.pdf
[5] J. Hightower and G. Borriello, “Location systems for ubiquitous computing,” Proceedings of IEEE Computer Magazine, No. 34:57–66, 2001.
[6] K. Pahlavan, X. Li, and J. Makela, “Indoor geolocation science and technology,” Proceedings of IEEE Communications Magazine, No. 40:112–118, 2002.
[7] Y. Zhao, “Mobile phone location determination and its impact on intelligent transportation systems,” Proceedings of IEEE Transactions on Intelligent Transportation Systems, No. 1:55–64, 2000.
[8] Guolin Sun, Jie Chen, Wei Guo, and K.J.R. Liu, “Signal processing techniques in network-aided positioning: a survey of state-of-the-art positioning designs,” Proceedings of IEEE Signal Processing Magazine, No. 22:12–23, 2005.
[9] A.H. Sayed, A. Tarighat, and N. Khajehnouri, “Network-based wireless location: challenges faced in developing techniques for accurate wireless location information,” Proceedings of IEEE Signal Processing Magazine, No. 22:24–40, 2005.
[10] T. S. Rappaport, J. H. Reed, and D. Woerner, “Position location using wireless communications on highways of the future,” Proceedings of IEEE Communications Magazine, No. 34:33–41, 1996.
[11] Tsung-Nan Lin and Po-Chiang Lin, “Performance comparison of indoor positioning techniques based on location fingerprinting in wireless networks,” Proceedings of International Conference on Wireless Networks, Communications and Mobile Computing, pp. 1569–1574, 2005.
[12] Shih-Hau Fang and Tsung-Nan Lin, “Projection-Based Location System via Multiple Discriminant Analysis in Wireless Local Area Networks,” Proceedings of IEEE Transaction on Vehicular Technology, vol. 58, pp. 5009 - 5019, 2009.
[13] Nerguizian, C. ; Despins, C. ; Affes, S., “Geolocation in mines with an impulse response fingerprinting technique and neural networks,” Proceedings of IEEE Transactions on Wireless Communications, vol. 5, pp. 603 - 611,2006.
[14] Teemu Roos, Petri Myllymki, Henry Tirri, Pauli Misikangas and Juha Sievanen, “A probabilistic approach to WLAN user location estimation,” Proceedings of International Journal of Wireless Information Networks, vol. 9, no. 3, July 2002.
[15] Yiqiang Chen, Qiang Yang, Jie Yin and Xiaoyong Chai, “Power-efficient access-point selection for indoor location estimation,” Proceedings of IEEE Transactions on Knowledge and Data Engineering, vol. 18, pp. 877 - 888, 2006.
[16] Shih-Hau Fang and Tsung-Nan Lin, “Accurate Indoor Location Estimation by Incorporating the Importance of Access Points in Wireless Local Area Networks,” Proceedings of Global Telecommunications Conference (GLOBECOM 2010), pp. 1-5, 2010.
[17] Hui Wang, Huaibei Zhou, Li Zhu, Qing He and Zairong Tian, “Analysis and Research on Indoor Positioning Method Based on IEEE 802.11,” Proceedings of Wireless Communications, Networking and Mobile Computing, pp. 2181-2183, Sep, 2007.
[18] Hashemi. H, “The indoor radio propagation channel,” Proceedings of the IEEE, vol. 81, pp. 943- 968, 1993.
[19] Azadeh Kushki, Konstantinos N. Plataniotis and Anastasios N. Venetsanopoulos, “Kernel-Based Positioning in Wireless Local Area Networks,” Proceedings of IEEE Transactions on Mobile Computing, vol. 6, pp. 689-705, 2007.
[20] M.A. Youssef, A. Agrawala, and A. Udaya Shankar, “WLAN location determination via clustering and probability distributions,” Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, pp. 143-150, 2003.
[21] SKYWORK, “Data Sheet:SKY13322-375LF: 20 MHz-6.0 GHz GaAs SP4T Switch,” [Online]. http://www.skyworksinc.com/uploads/documents/201098D.pdf
[22] Shiann-Tsong Sheu, Ming-Tse Kao, Yen-Ming Hsu and Yen-Chieh Cheng, “Indoor Location Estimation Using Smart Antenna System,” Proceedings of the IEEE Vehicular Technology Conference (VTC Fall 2013), pp. 1-5, Sep. 2013.
[23] A. Gelman et al., “Bayesian Data Analysis,” second ed. Chapman and Hall, 2004.
[24] D. Madigan et al., “Bayesian Indoor Positioning Systems,” Proceedings of the 24th Annual Joint Conference of the IEEE Computer and Communications Societies, pp. 1217-1227, Mar. 2005
[25] Genming Ding, Zhenhui Tan, Jinbao Zhang and Lingwen Zhang, “Regional Propagation Model Based Fingerprinting Localization in Indoor Environments,” Proceedings of the IEEE 24th International Symposium on Personal, Indoor and Mobile Radio Communications: Fundamentals and PHY Track, Sep. 2013
指導教授 許獻聰(Hsien-tsung Hsu) 審核日期 2014-7-29
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明