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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 孫雅麗 老師(Yea-Li Sun) | |
| dc.contributor.author | Wei-Ting Hsu | en |
| dc.contributor.author | 徐維廷 | zh_TW |
| dc.date.accessioned | 2021-06-17T00:12:17Z | - |
| dc.date.available | 2012-10-01 | |
| dc.date.copyright | 2012-07-18 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-07-11 | |
| dc.identifier.citation | [1] http://www.skyhookwireless.com/
[2] “RSS-based localization in environments with different path loss exponent for each link,” J. Shirahama and T. Ohtsuki, Proc. IEEE Vehicular Technology Conference, 2008 [3] “Localization in WiMAX Networks Based on Signal Strength Observations,” Mussa Bshara, Nico Deblauwe and Leo Van Biesen, IEEE Globecom, 2008 [4] “Sensor Measurements for Wi-Fi Location with Emphasis on Time-of-Arrival Ranging,” Stuart A. Golden and Steve S. Bateman, IEEE Transactions on Mobile Computing, 2007 [5] “A Generalized Subspace Approach for Mobile Positioning With Time-of-Arrival Measurements,” H. C. So and Frankie K. W. Chan, IEEE Transactions on Signal Processing, 2007 [6] “Fingerprinting-Based Localization in WiMAX Networks Depending on SCORE Measurements,” Mussa Bshara and Leo Van Biesen, IEEE Telecommunications, 2009 [7] “Cooperative Mobile Positioning and Tracking in Hybrid WiMAX/WLAN Networks,” Francescantonio Della Rosa, 2007 [8] “Cooperative Positioning Techniques for Mobile Localization in 4G Cellular Networks,” Carlos Leonel Flores Mayorga, Francescantonio Della Rosa, Satya Ardhy Wardana, Gianluca Simone, Marie Claire Naima Raynal, Jo˜ao Figueiras and Simone Frattas, IEEE International Conference on Pervasive Services, 2007 [9] “Available Measurements in Current WiMAX Networks and Positioning Opportunities,” Mussa Bshara and Leo Van Biesen, IMEKO, 2009 [10] “Smartphone-Based Collaborative and Autonomous Radio Fingerprinting,” Yungeun Kim, Yohan Chon and Hojung Cha, IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 2010 [11] “A Comparative Survey of WLAN Location Fingerprinting Methods,” Ville HONKAVIRTA, Tommi PER‥AL‥A, Simo ALI-L‥OYTTY and Robert PICH’E, Proc. IEEE Workshop on Positioning, Navigation and Communication, 2009 [12] “RADAR: An in-building RF-based user location and tracking system,” P. Bahl and V. Padmanabhan, Proc. IEEE Conference on Computer Communications, 2000 [13] “Modeling of indoor positioning systems based on location fingerprinting,” K. Kaemarungsi and P. Krishnamurthy, Proc. IEEE Conference on Computer Communications, 2004 [14] “Indoor positioning techniques based on wireless LAN,” B. Li, J. Salter, A. G. Dempster and C. Rizos, IEEE International Conference on Wireless Broadband and Ultra Wideband Communications, 2006 [15] “Location determination of a mobile device using IEEE 802.11b access point signals,” S. Saha, K. Chauhuri, D. Sanghi and P. Bhagwat, IEEE Wireless Communications and Networking, 2003 [16] “Fingerprinting Localization in Wireless Networks Based on Received-Signal-Strength Measurements: A Case Study on WiMAX Networks,” Mussa Bshara, Umut Orguner, Fredrik Gustafsson and Leo Van Biesen, IEEE Transactions on Vehicular Technology, 2009 [17] “VTrack: Accurate, Energy-aware Road Traffic Delay Estimation Using Mobile Phones,” Arvind Thiagarajan, Lenin Ravindranath, Katrina LaCurts, Samuel Madden, Hari Balakrishnan, Sivan Toledo and Jakob Eriksson, ACM SenSys, 2009 [18] “Accurate, Low-Energy Trajectory Mapping for Mobile Devices,” Arvind Thiagarajan, Lenin Ravindranath, Hari Balakrishnan, Samuel Madden and Lewis Girod, Proc. Usenix Symposium Networked Systems Design and Implementation, 2011 [19] “Unsupervised Construction of Indoor Floor Plan Using Smartphone,” Hyojeong Shin, John Chon and Hojung Cha, IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 2011 [20]“Cellsense: A probabilistic rssi-based gsm positioning system,” M. Ibrahim and M. Youssef, IEEE Globecom, 2010 [21] “A Hidden Markov Model for Localization Using Low-End GSM Cell Phones,” Mohamed Ibrahim and Moustafa Youssef, IEEE International Conference on Communications, 2011 [22] “Robust Tracking in Cellular Networks Using HMM Filters and Cell-ID Measurements,” Mussa Bshara, Umut Orguner, Fredrik Gustafsson and Leo Van Biesen, IEEE Transactions on Vehicular Technology, 2011 [23] “WiFi-SLAM using gaussian process latent variable models,” Ferris B., Fox D. and Lawrence N., Proc. International Joint Conference on Artificial Intelligence, 2007 [24] “Manifold regularization: A geometric framework for learning from labeled and unlabeled examples,” Belkin M., Niyogi P. and Sindhwani V., Journal of Machine Learning Research 7, 2006 [25] “Co-localization from labeled and unlabeled data using graph laplacian,” Pan J. J. and Yang Q., Proc. International Joint Conference on Artificial Intelligence, 2007 [26]“Transferring Localization Models Across Space,” Sinno Jialin Pan, Dou Shen, Qiang Yang and James T. Kwok, Proc. AAAI Conference on Artificial Intelligence, 2008 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/65798 | - |
| dc.description.abstract | 近年來由於行動裝置的數量不斷攀升，適地性服務 (location-based services)跟著興起，這使得定位準確性和效率變成越趨重要的議題。全球定位系统 (Global Positioning System, GPS)是最廣為人知的定位技術。GPS在開放空間擁有相當高的準確性，但都市戶外大樓林立，GPS的準確性和可達性會因為樓房障礙物的存在而降低。除了GPS定位之外，實務和研究領域都有相當多使用無線訊號的技術，這些訊號包括全球行動通訊系統 (Global System for Mobile Communication)、Wi-Fi和全球互通微波存取 (Worldwide interoperability for Microwave Access)。
相關無線訊號定位研究主要都測重訊號強度特徵比對，但我們發現對同一個地點而言，某些基地台的訊號強度會隨著天氣而改變，另外，無線訊號強度也都一定程度受大樓障礙物所影響。我們提出一個健全且新穎的定位方法，該定位方法利用前次與今次定位差的回溯方式混合了一個既有的K最近鄰法(K nearest neighbors)，和兩個我們提出的格框平均訊號強度比對法 (Grid average Signal Strength Matching)及考量天氣因素訊號差比對方法 (Signal Difference Matching)。我們的研究包含了完整的實驗流程和方法，同時也和多種定位方法比較，這其中包含了決定性(deterministic) 和機率性 (probabilistic) 特徵比對法 (fingerprinting technique)。我們方法的中位誤差是52公尺，美國聯邦通信委員會準確性和穩定度都比其他方法好。該方法考量了天氣因素，且無需任何基地台的位置資訊，且可以用於沒有GPS的行動裝置上或作為輔助定位之用。 | zh_TW |
| dc.description.abstract | Due to the rising amount of mobile devices and location-based services (LBSs), the accuracy and efficiency of localization become a more and more important issue. Global Positioning System (GPS) is the most well-known positioning techniques; it can provide highly accurate location results in open sky environments, but its accuracy and availability declines in outdoor urban canyons. Beside GPS, radio signals are used for localization both in practice and in research; these signals include Global System for Mobile Communication (GSM), Wi-Fi and Worldwide interoperability for Microwave Access (WiMAX).
Related work of wireless positioning mostly rely on signal strength, but we know that signal strength differs in different weathers; reflection and signal decay when passing through obstacles are also problems. Thus, we proposed a robust positioning method using the hybrid of a deterministic method K nearest neighbor(s) (KNN) and two novel techniques including Grid average Signal Strength (SS) Matching and Signal Difference Matching for location estimation, which puts weather conditions into concerns. Also, we performed a complete experiment and done accuracy and robustness evaluation of our positioning method along with other deterministic and probabilistic techniques. Our positioning method reaches a median error 52 meters, which is much superior than the accuracy requirement (latitude and longitude of emergency callers within 300 meters) of Wireless enhanced 911 from the U.S. Federal Communications Commission (FCC), and shows better accuracy and robustness than others; it doesn’t require any pre-knowledge of base stations and can be used on GPS-free mobile device or supporting GPS. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T00:12:17Z (GMT). No. of bitstreams: 1 ntu-101-R99725052-1.pdf: 1509399 bytes, checksum: 39777c8c35dbd2a35556a7dc66b69649 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | Chapter 1. Introduction p.1
1.1 Background and Motivation p.1 1.2 Methodology Briefing and Contributions p.3 1.3 Thesis Structure p.4 Chapter 2. Related Work p.5 2.1 Physical Measurements of Radio Propagation Techniques p.5 2.2 Fingerprints Techniques p.5 2.2.1 Deterministic Techniques p.6 2.2.2 Probabilistic Techniques p.6 2.3 Learning-based Techniques p.8 Chapter 3. Methodology Overview and Preprocessing p.10 3.1 Data Model p.12 3.2 Framework p.13 3.3 Preprocessing p.15 3.3.1 Ground Truth GPS Filter p.15 3.3.2 GPS Interpolation p.16 3.3.3 Vector Format Transformation p.17 3.3.4 Difference Format Transformation p.18 Chapter 4. Positioning and Evaluation Methods p.20 4.1 Similarity Tool Used p.20 4.2 Positioning Method p.21 4.2.1 Grid Average Signal Strength Matching p.21 4.2.2 Signal Difference Matching p.22 4.2.3 K Nearest Neighbors Matching p.24 4.2.4 Hybrid Method p.24 4.3 Evaluation Methods p.26 4.3.1 Histogram Likelihood Matching p.26 4.3.2 Hidden Markov Model p. 27 Chapter 5. Experiment and Evaluation p.29 5.1 Equipment p.29 5.2 Data and Experiment Settings p.32 5.3 Evaluation Results and Analysis p.34 5.3.1 Grid Length Effect p.35 5.3.2 Top K Effect p.36 5.3.3 Positioning Methods Comparisons and Top K Effect p.37 5.3.4 Robustness p.48 Chapter 6. Conclusions and Future Work p.50 References p.51 | |
| dc.language.iso | en | |
| dc.title | 利用WiMAX訊號特徵比對的健全定位方法 | zh_TW |
| dc.title | A Robust Positioning Method using WiMAX Fingerprints | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 陳孟彰 老師(Meng-Chang Chen) | |
| dc.contributor.oralexamcommittee | 陳建錦 老師(Chien-Chin Chen),彭文志 老師(Wen-Chih Peng) | |
| dc.subject.keyword | 定位,定位準確度,健全性天氣,訊號差,地點,位置,特徵比對,決定性,機率性,WiMAX,GPS,適地性服務,行動裝置,運輸, | zh_TW |
| dc.subject.keyword | Positioning,Positioning Accuracy,Robust,Weather,Signal Delta,Signal Difference,Location,Localization,Fingerprinting,Deterministic,Probabilistic,WiMAX,GPS,LBS,Mobile Device,Transportation, | en |
| dc.relation.page | 53 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2012-07-12 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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| ntu-101-1.pdf 目前未授權公開取用 | 1.47 MB | Adobe PDF |
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