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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 朱浩華 | |
dc.contributor.author | Wen (Arvin) Tsui | en |
dc.contributor.author | 崔文 | zh_TW |
dc.date.accessioned | 2021-06-17T00:43:21Z | - |
dc.date.available | 2012-02-16 | |
dc.date.copyright | 2012-02-16 | |
dc.date.issued | 2012 | |
dc.date.submitted | 2012-01-13 | |
dc.identifier.citation | [1] F. Alizadeh-Shabdiz, R. K. Jones, E. J. Morgan and M. G. Shean, “Location-based services that choose location algorithms based on number of detected access,” US Patnet 7,305,245 B2, Dec. 4, 2007.
[2] P. Bahl and V. N. Padmanabhan. “RADAR: An in-building RF-based user location and tracking system,” in Proceedings of Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM), Mar. 2000. [3] P. Bahl, V. N. Padmanabhan and A. Balachandran. “Enhancements to the RADAR user location and tracking system,” Microsoft Research Technical Report: MSR-TR-00-12, February 2000. [4] X. Chai and Q. Yang. “Reducing the calibration effort for location estimation using unlabeled samples,” in Proceedings of the First IEEE International Conference on Pervasive Computing and Communications (PERCOM 2005), 2005. [5] Li-wei Chan, Ji-rung Chiang, Yi-chao Chen, Chia-nan Ke, Jane Hsu, and Hao-hua Chu, 'Collaborative localization — enhancing WiFi-based position estimation with neighborhood links in clusters,' in Proceedings of the International Cconference on Pervasive Computing (PERVASIVE 2006), 2006, pages 50–66. [6] M. Y. Chen, T. Sohn, D. Chmelev, D. Haehnel, J. Hightower, J. Hughes, A. LaMarca, F. Potter, I. Smith and A. Varshavsky, “Practical metropolitan-scale positioning for GSM phones,” in Proc. 7th Int. Conf. Ubiquitous Computing (UbiComp), 2006. [7] Yi-chao Chen, Ji-rung Chiang, Hao-hua Chu, Polly Huang, and Arvin Wen Tsui, 'Sensor-assisted Wi-Fi indoor location system for adapting to environmental dynamics,' in Proceedings of ACM International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWIM 2005), 2005, pages 118–125. [8] Y.C. Cheng, Y. Chawathe, A. LaMarca and J. Krumm. “Accuracy characterization for metropolitan-scale Wi-Fi localization,” in Proceedings of the Third International Conference on Mobile Systems, Applications and Services (MobiSys), Seattle, WA, June, 2005. [9] J. Chung, M. Donahoe, C. Schmandt, I. J. Kim, P. Razavai and M. Wiseman, “Indoor Location Sensing Using Geo-Magnetism,” In Proceedings of the 9th international conference on Mobile systems, applications, and services (MobiSys ’11), 2011, Bethesda, Maryland, USA. [10] “DGPS” Internet: http://en.wikipedia.org/wiki/DGPS [Jan. 08, 2010] [11] El-Gallad, M. El-Hawary, and A. Sallam. “Swarming of intelligent particles for solving nonlinear constrained optimization problem,” Intemational Journal of Engineering Intelligent Systems, Vol. 9, No. 3, pp.155–163, Sept. 2001. [12] “FCC Wireless 911 Sevice,” Internet: http://www.fcc.gov/guides/wireless-911-services/ [Dec. 26, 2011] [13] D. Fox, J. Hightower, L. Liao, D. Schulz, and G. Borriello, “Bayesian Filtering for Location Estimation,” in Proceedings of IEEE Pervasive Computing, 2003, pp. 24–33. [14] G. N. Frederickson. “Approximation algorithms for some postman problems,” Jouranl of ACM 26(3):538-554, Jul. 1979. [15] “Garmin estimation of WAAS accuracy” Internet: http://www8.garmin.com/aboutGPS/waas.html [Jan.08, 2010] [16] Haeberlen, E. Flannery, A. M. Ladd, A. Rudys, D. S. Wallach and L. E. Kavraki. “Practical robust localization over large-scale 802.11 wireless networks,” in Proceedings of ACM MOBICOM, 2004. [17] Harter, A. Hopper, P. Steggles, A. Ward and P. Webster. “The Anatomy of a Context-Aware Application,” in Proc. 5th Ann. Int’l Conf. Mobile Computing and Networking (Mobicom 99), pp. 59-68, Seattle, 1999. [18] H. Hashemi. “The indoor radio propagation channel,” in Proceedings of the IEEE, 81:943-968, July 1993. [19] J. Hightower and G. Borriello. “Particle filters for location estimation in ubiquitous computing: A case study,” in Proceedings of International conference on Ubiquitous Computing (UbiComp), 2004. [20] M. Ibrahim and M. Youssef, “CellSense: A probabilistic RSSI-based GSM positioning system,” in IEEE Globecom, 2010. [21] R.K. Jones, F. Alizadeh-Shabdiz, E. J. Morgan and M. G. Shean, “Server for updating location beacon database,” U.S. Patent US 7,414,988 B2, Aug. 19, 2008. [22] K. Jones and L. Liu. “What where wi: an analysis of millions of Wi-Fi access points,” in Proceedings of IEEE Portable Devices, 2007. [23] D. H. Kim, J. Hightower, R. Govinden, and D. Estrin. “Discovering Semantically Meaningful Places from Pervasive RF-Beacons,” in Proceedings of UbiComp ‘09, Orlando (2009) [24] M. Kim, J. J. Feilding and D. Kotz. “Risks of using AP locations discovered through war driving,” in Proceedings of 4th International Conference on Pervasive Computing, Dublin Ireland, May, 2006. [25] M. B. Kjargaard, S. Bhattacharya, H. Blunck and P. Nurmi, “Energy-efficient Trajectory Tracking for Mobile Devices,” In Proceedings of the 9th international conference on Mobile systems, applications, and services (MobiSys ’11), 2011, Bethesda, Maryland, USA. [26] M. Kjaergaard and C. Munk, “Solving RSS client differences by hyperbolic location fingerprinting,” in Proceedings of IEEE International Conference on Pervasive Computing, 2008. [27] P. Krishnan, A. Krishnakumar, W.-H. Ju, C. Mallows and S. Ganu. “A system for LEASE: Location estimation assisted by stationary emitters for indoor RF wireless networks,” in Proceedings of IEEE INFOCOM, 2004. [28] J. Krumm, G. Cermak, and E. Horvitz, “Rightspot: A novel sense of location for a smart personal object,” Proceedings of Ubicomp 2003. LNCS 2864. Pages 36-43. [29] J. Krumm and K. Hinckley, “The NearMe Wireless Proximity Server,” in Proceedings of the Sixth International Conference on Ubiquitous Computing, 2004. [30] M. Ladd, K. E. Bekris, A. Rudys, G. Marceau, L. E. Kavraki and D. S. Wallach. “Robotics-based location sensing using wireless Ethernet,” in Proceedings of the Eighth Annual International Conference on Mobile Computing and Networking (MOBICOM), Atlanta, GA, Sept. 2002. [31] LaMarca, Y. Chawathe, S. Consolvo, J. Hightower, I.E. Smith, J. Scott, T. Sohn, J. Howard, J. Hughs, F. Potter, J. Tabert, P. Powledge, G. Borriello and B. N. Schilit, “Place Lab: Device Positioning Using Radio Beacons in the Wild,” in Proceedings of Pervasive ’05, pp. 116-133, Munich (2005) [32] Tsung-han Lin, Ju-peng Chen, Hsing-hau Chen, Polly Huang, and Hao-hua Chu, 'Enabling energy-efficient and quality localization services,' in Proceedings of the IEEE International Conference on Pervasive Computer and Communications (PERCOM 2006), Work In Progress Session, 2006. [33] T. Michell, Machine Learning, McGraw Hill, 1993, pp. 239–240. [34] Y. Ma, R. Hankins and D. Racz, “iLoc: A Framework for Incremental Location-State Acquisition and Prediction Based on Mobile Sensors,” In Proceedings of 18th ACM Conf. Information and Knowledge Management (CIKM), 2009, pp. 1367–1376. [35] A. Matic, A. Papliatseyeu, V. Osmani and O. Mayora-Ibarra, “Tuning to your position: FM radio based indoor localization with spontaneous recalibration”, in Proceedings of Pervasive Computing and Communications (PerCom), 2010, Mannheim, pp. 153-161. [36] P. Misikangas and L. Lekman, “Applications of Signal Quality Observations,” Patent Cooperation Treaty (PCT) WO 2004/008796 A1, Jul. 8, 2003. [37] M. Modsching, R. Kramer and K. T. Hagen. “Field Trial on GPS Accuracy in a Medium Size City: The Influence of Built-up,” in Proceedings of the 3rd Workshop on Positioning, Navigation and Communication, pp. 209-218 (2006) [38] E. J. Morgan, F. Alizadeh-Shadbiz, R. K. Jones and M. G. Shean, “Method and system for building a location beacon database,” US 2006/0095349 A1, May. 4, 2006. [39] “M-Taiwan project offical website” Internet: http://www.mtaiwan.org.tw/eng/index.php [Jan. 08, 2010] [40] Navizon. http://www.navizon.com. [41] V. Otsason, A. Varshavsky, A. L. Marca, and E. de Lara. “Accurate GSM indoor localization,” in Proceedings of the Seventh International Conference on Ubiquitous Computing , 2005. [42] J. Paek, K-H Kim, J. P. Singh and R. Govindan, “Energy-Efficient Positioning for Smartphones using Cell-ID Sequence Matching,” In Proceedings of the 9th international conference on Mobile systems, applications, and services (MobiSys ’11), 2011, Bethesda, Maryland, USA. [43] K. Pearson. “Mathematical contribution to the theory of evolution VII: On the correlation of characters not quantitatively measurable,” Philosophical Transactions of the Royal Society, Series A, 1900,195, 1–47. [44] M. Pettinari. “Context detection and abstraction in smart environment,” Ph. D. Thesis, http://amsdottorato.cib.unibo.it/1123/1/Tesi_Pettinari_Marina.pdf, pp. 66-109 (2007) [45] “Place engine,” Internet: http://www.placeengine.com/en [Dec. 26, 2011] [46] W. ur Rehman , E. de Lara and S. Saroiu, “CILoS: a CDMA indoor localization system,” In Proceedings of the 10th international conference on Ubiquitous computing, 2008, Seoul, Korea, pp. 104-113. [47] T. Roos, P. Myllymaki, H. Tirri, P. Misikangas and J. Sievanan. “A probabilistic approach to WLAN user location estimation,” International Journal of Wireless Information Networks, 9(3), July 2002. [48] D. Schulz, D. Fox, and J. Hightower, “People Tracking with Anonymous and ID-Sensors using Rao-Blackwellised Particle Filters,” in Proceedings of IJCAI, 2003, pp. 921–926. [49] Y. Scott Seidel and S. Theodore. Rapport. “914 MHz path loss prediction model for indoor wireless communications in multifloored buildings,” IEEE Transactions on Antennas and Propagation, 40(2):207-217, 1992. [50] V. Seshadri, G. V. Zaruba, and M. Huber, “A Bayesian Sampling Approach to In-door Localization of Wireless Devices Using Received Signal Strength Indication,” in Proceedings of IEEE International Conference on Pervasive Computing (PERCOM 2005), 2005. [51] P. Shipley. “Open WLANS: the early results of war driving,” Internet: http://www.dis.org/filez/openlans.pdf. Oct. 2001. [52] “Skyhook Wireless Corporate Website” Internet: http://www.skyhookwireless.com/ [Jan. 08, 2010] [53] P. Tao, A. Rudys, A. M. Ladd, and D. S. Wallach. “Wireless LAN location–sensing for security applications,” in Proceedings of the AAAI Thirteenth National Conference on Artificial Intelligence, Aug. 1996. [54] S. Tekinay, “Special issue on Wireless Geolocation Systems and Services,” IEEE Communications Magazine, April 1998. (E911 tech) [55] N. O. Tippenhauer, K. B. Rasmussen, C. Popper and S. Capkun “Attacks on public WLAN-based positioning systems,” In Proceedings of the ACM/Usenix International Conference on Mobile Systems, Applications and Services (MobiSys ’09), 2009, Krakow, Poland. (Skyhook attack) [56] S. Thrun, “Probabilistic algorithms in robotics,” AI Magazine, 21(4):93–109, 2000. [57] S. Thrun. “Probabilistic Robotics,” Communications of the ACM, 45(3), March 2002. [58] H. Wang, H. Lenz, A. Szabo, J. Bamberger and U. Hanebeck. “WLAN-based pedestrian tracking using particle filters and low-cost MEMS sensors,” in Proceedings of the 4th Workshop on Positioning, Navigation and Communication, 2007. WPNC 2007, pp. 1–7 (2007) [59] Y. Wang, X. Jia and H.K. Lee. “An indoors wireless positioning system based on wireless local area network infrastructure,” in Proceedings of the 6th International Symposium on Satellite Navigation Technology Including Mobile Positioning & Location Services (SatNav '03), 2003. [60] “WeFi,” Internet: http://www.wefi.com/ [Dec. 26, 2011] [61] H. Weinberg. “Using the adxl202 in Pedometer and Personal Navigation Applications,” APPLICATION NOTE, Analog Device. (2002) [62] O. Woodman and R. Harle. “Pedestrian localisation for indoor environments,” in Proceedings of the 10th international conference on Ubiquitous computing, pp. 114-123,Seoul, Korea. [63] H. Wu, K. Tan, J. Liu, and Y. Zhang, “Footprint: Cellular Assisted Wi-Fi AP Discovery on Mobile Phones for Energy Savings,” In Proceedings of ACM WiNTECH, 2009. [64] J. Yang, A. Varshavsky, H. Liu, Y. Chen and M. Gruteser, “Accuracy Characterization of Cell Tower Localization,” In Proceedings of the 12th international conference on Ubiquitous computing, 2010. [65] J. Yin, Q. Yang and L. Ni. “Adaptive temporal radio maps for indoor location estimation,” in Proceedings of the First IEEE International Conference on Pervasive Computing and Communications (PERCOM 2005), 2005. [66] H. Yoshida, S. Ito, N. Kawaguchi, “Evaluation of pre-acquisition methods for position estimation system using wireless LAN,” in Proceedings of the Third International Conference on Mobile Computing and Ubiquitous Networking (ICMU 2006), London, UK, October 2006, pp. 148–155. [67] Chuang-wen You, Yi-Chao Chen, Ji-Rung Chiang, Polly Huang, Hao-hua Chu, and Seng-Yong Lau, 'Sensor-enhanced mobility prediction for energy-efficient localization,' in Proceedings of Third Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON 2006), 2006. [68] P. A. Zandbergen, “Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning,” Transactions in GIS, 2009, 13(s1): 5–26 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66562 | - |
dc.description.abstract | Wi-Fi訊號特徵定位法(fingerprint localization)之準確度與訊號地圖(radio map)的品質息息相關。本論文分析了多個都會區訊號地圖可能影響定位準確度的因素。在我們的研究中分別以步行與駕車的方式大規模地收集了都會範圍的訊號地圖,其中包括了數十萬個Wi-Fi基地台以及數以百萬計的訊號樣本。藉由對步行與駕車收集的訊號地圖所進行的深入分析與比較,我們找到了許多影響定位準確度的關鍵因素,在得知這些因素的影響程度後,根據這些分析結果設計了有效的改善方法。此外,硬體裝置的差異也是影響Wi-Fi定位的重要因素,雖然可以藉由手動調整來改善,但因為裝置不斷推陳出新而無法大量實做,我們提出了非人力介入 (unsu-pervised) 的自動演算法來解決這個問題,實驗結果顯示,自動演算學習的過程只需不到100秒即可收斂。我們更進一步根據定位信心度指標設計了混和型的行人定位系統,使得定位準確度的中位數可以達到十公尺以內,對GPS在都市叢林中準度下降的情形 (urban canyon problem) 提供了良好的輔助效果。這是藉由整合Wi-Fi定位系統以及手機上的加速感應器與電子羅盤所達成,這個系統的最終準確度達到中位數8.6公尺的水準。 | zh_TW |
dc.description.abstract | The accuracy of a Wi-Fi-fingerprint localization system critically depends on the quality of its radio map. This thesis explores various properties of a metropolitan radio map that may affect the accuracy of Wi-Fi-fingerprint localization systems. In our study, metro-politan-scale radio maps are obtained using war walking and war driving. These maps contain hundreds of thousands of access points and signal samples. A detailed comparison analysis of selected radio map properties reveals how different map properties affect the difference between the positional accuracies of the driving and walking radio maps in Wi-Fi fingerprint-based localization. Hardware variance can significantly degrade the positional accuracy of RSS-based Wi-Fi localization systems. Although manual adjust-ment can reduce positional error, this solution is not scalable as the number of new Wi-Fi devices increases. We propose an unsupervised learning method to automatically solve the hardware variance problem in Wi-Fi localization. Experimental results demonstrate that the proposed learning method improves positional accuracy within 100 seconds of learning time. To further optimize the metropolitan Wi-Fi localization algorithm, we propose a confidence-based pedestrian localization system that can achieve <10 meters average positional accuracy on a mobile phone in the outdoor environments of cities where GPS may perform poorly under urban canyon. Our pedestrian localization system works by combining an outdoor Wi-Fi-based localization method and a relative localization method based on accelerometers/digital compass signals. Experimental results show that our hybrid pedestrian localization system achieved a median positional accuracy of 8.6 meters. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T00:43:21Z (GMT). No. of bitstreams: 1 ntu-101-D95944006-1.pdf: 4953315 bytes, checksum: 6b50fdead5f2640735434fc41125a9b3 (MD5) Previous issue date: 2012 | en |
dc.description.tableofcontents | Contents
論文口試委員審定書 II 誌謝 III 摘要 IV ABSTRACT V LIST OF FIGURES VIII LIST OF TABLES XI CHAPTER 1 INTRODUCTION 1 1.1 MOTIVATION 1 1.2 THESIS CONTRIBUTION 8 1.3 THESIS OUTLINE 9 CHAPTER 2 METROPOLITAN WI-FI RADIO MAPS IN TAIWAN 10 2.1 DATA COLLECTION: SCOPE AND METHODS 11 2.2 ACCESS POINT DENSITY AND RSS DISTRIBUTION 16 2.3 ANALYSES 20 2.3.1 Temporal Changes 20 2.3.2 Speed Test 23 2.3.3 City variance 24 2.4 DISCUSSION 27 CHAPTER 3 ACCURACY PERFORMANCE ANALYSIS BETWEEN WAR DRIVING AND WAR WALKING 30 3.1 DATA COLLECTION 30 3.2 METROPOLITAN WI-FI LOCALIZATION ALGORITHM 34 3.2 COMPARISON BETWEEN METROPOLITAN DRIVING AND WALKING RADIO MAPS 38 3.2.1 Signal Strength of Detected APs 40 3.2.2 Amount of Signal Data 43 3.2.3 GPS Labeling of Signal Samples 48 3.2.4 Uniformity of Calibration Points on Radio Maps 50 3.3 IMPROVING THE DRIVING RADIO MAP 54 3.3.1 Obtaining Multiple Samples 54 3.3.2 Confidence-based Remedy Method 55 CHAPTER 4 UNSUPERVISED LEARNING FOR SOLVING RSS HARDWARE VARIANCE PROBLEM 59 4.1 RATIONALE 59 4.1.1 Signal-Pattern Variations of Wi-Fi Client Devices 60 4.1.2 Effect of Linear Signal-Pattern Shift on the Accuracy of a Wi-Fi Positioning System 63 4.1.3 Signal-Pattern Transformation Function 63 4.1.4 Unsupervised Learning 65 4.2 DESIGN AND IMPLEMENTATION 66 4.2.1 On-Line Regression Algorithm 67 4.2.2 EM Algorithm 68 4.2.3 Neural Network Algorithm 69 4.2.4 Extended EM Algorithm 70 4.3 EVALUATION 70 4.3.1 Experimental Setup 71 4.3.2 Positional Accuracy 71 4.3.3 Training Time 73 4.3.4 Case study: the Orinoco as the training device 74 4.3.5 Case study: similarity between tracking/training devices 75 4.3.6 Case study: variable-speed vs. constant-speed movement 76 4.3.7 Relationship between RSS signal-pattern difference and positional error 77 4.3.8 Analytical model for the balanced AP distribution 79 CHAPTER 5 FURTHER OPTIMIZATIONS 82 5.1 PROBABILISTIC METROPOLITAN WI-FI LOCALIZATION ALGORITHMS 82 5.2 SPATIAL HINTS 85 5.3 MODIFIED PROBABILISTIC MODEL 87 5.4 EVALUATION 90 CHAPTER 6 PRACTICAL HYBRID LOCALIZATION FOR PEDESTRIANS USING MOBILE PHONES 91 6.1 SENSOR AND WI-FI-BASED LOCALIZATION 91 6.2 SIMPLE HYBRID LOCALIZATION 93 6.3 CONFIDENCE-BASED HYBRID LOCALIZATION 94 6.4 EXPERIMENTS 98 CHAPTER 7 RELATED WORK 100 CHAPTER 8 CONCLUSION AND FUTURE WORK 107 BIBLIOGRAPHY 110 | |
dc.language.iso | en | |
dc.title | 都會範圍Wi-Fi定位準確度分析與改進之研究 | zh_TW |
dc.title | Accuracy Analyses and Improvements on Metropolitan-Scale Wi-Fi Localization | en |
dc.type | Thesis | |
dc.date.schoolyear | 100-1 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 許永真,黃寶儀,王傑智,陳伶志,藍崑展 | |
dc.subject.keyword | Wi-Fi定位技術,訊號特徵定位法,都會區定位技術,訊號地圖,駕車收訊,步行收訊,行人定位技術, | zh_TW |
dc.subject.keyword | Wi-Fi localization,Fingerprint-based localization,metropolitan localization,radio maps,war driving,war walking,pedestrian localization, | en |
dc.relation.page | 115 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2012-01-13 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
顯示於系所單位: | 資訊網路與多媒體研究所 |
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