政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/32647

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/32647

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题名:	以減少測量數為目標之無線網路定位系統 Reducing Calibration Effort for WLAN Location and Tracking System
作者:	李政霖 Li, Cheng-Lin
贡献者:	蔡子傑 Tsai, Tzu-Chieh 李政霖 Li, Cheng-Lin
关键词:	無線網路定位系統無線訊號傳遞模型學習模型自我相關度 Indoor Locating System Wireless Channel Propagation Model Learning Model Autocorrelation
日期:	2005
上传时间:	2009-09-17 13:55:50 (UTC+8)
摘要:	內容感知的應用在今日已經變的越來越熱門，而位置資訊的可知也因此衍生出許多研究的議題。這篇論文提出了一套精準的室內無線網路系統名為Precise Indoor Location System (PILS)。大部分擁有良好定位精準度的定位系統都必須在事情花費許多的人力在收集大量的訊號上面，使得定位系統的變的不實用與需求過多的人力資源。在這篇論文裡，我們將目標放在減少在建置訊號地圖上的人力資源耗費並且保持住定位系統的精準度在一個可以接受的範圍。我們也提出了在資料收集上、訊號內插上、以及位置估計上的模型。另外我們也考慮了一連串連續訊號的相關度來提高準確度。無線網路訊號傳遞的特性也是我們研究的一部份，大小範圍的遮蔽包含在我們所研究的訊號傳遞現象裡面。最後我們提出了一套學習的模型來調整我們的訊號地圖，以改進因為測量數目的減少所造成的精準度下降。 Context-aware applications become more and more popular in today’s life. Location-aware information derives a lot of research issues. This thesis presents a precise indoor RF-based WLAN (IEEE 802.11) locating system named Precise Indoor Locating System (PILS). Most proposed location systems acquire well location estimation results but consume high level of manual efforts to collect huge amount of signal data. As a consequence, the system becomes impractical and manpower-wasted. In this thesis, we aim to reduce the manual efforts in constructing radio map and maintain high accuracy in our system. We propose the models for data calibration, interpolating, and location estimation in PILS. In the data calibration and location estimation models, we consider the autocorrelation of signal samples to enhance accuracy. Large scale and small scale fading are involved in the wireless channel propagation model. We also propose a learning model to adjust radio map for improving the accuracy down caused by calibrated data reduction.
參考文獻:	[1] A. Papoulis, “Probability, Random Variables, and Stochastic Processes”, McGraw-Hill, third edition, 1991. [2] Ali Taheri Arvinder Singh Emmanuel Agu, “Location Fingerprinting on Infrastructure 802.11Wireless Local Area Networks (WLANs) using Locus”, Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks (LCN’04). [3] Andreas Haeberlen, Eliot Flannery, Andrew M. Ladd, Algis Rudys, Dan S. Wallach, Lydia E. Kavraki, “Practical Robust Localization over Large-Scale 802.11 Wireless Networks”, MobiCom’04, Sept. 26-Oct. 1, 2004, Philadelphia, Pennsylvania, USA. [4] Ankur Agiwal, Parakram Khandpur, Huzur Saran, “LOCATOR - Location Estimation System For Wireless LANs”, WMASH’04, October 1, 2004, Philadelphia, Pennsylvania, USA. [5] Asim Smailagic and David Kogan, “Locating Sensing and Privacy In a Context-Aware Computing Environment”, in IEEE Wireless Communications, no. 5, Oct 2002, pp.10-17. [6] H. Hashemi, “The indoor radio propagation channel. In Proceedings of the IEEE”, volume 81, pages 943–968, 1993. [7] Isaac K Adusei and K.Kyamakya and Klaus Jobmann, “Mobile Positions Technologies in Cellular Networks: An Evaluation of their Performance Metrics”, in MILCOM 2002, Oct 2002, pp. 1239-1244. [8] J. Krumm and J. C. Platt, “Minimizing calibration effort for an indoor 802.11 device location measurement system”, Technical report, Microsoft Research, 2003. [9] Jon W. Mark, and Weihua Zhuang, “Wireless Communications and Networking”, Prentice Hall, 2003. [10] L. R. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition”, Proceedings of the IEEE, vol. 77, No. 2, February 1989 [11] L. R. Rabinerand B. H. Juang, (1993) Fundamentals of Speech Recognition, Chapter 6 [12] Ming-Hui Jin, Eric Hsiao-Kuang Wu, Yu-Ting Wang, Chin-Hua Hsu, “802.11-based Positioning System for Context Aware Applications”, Globecom2003. [13] Ming-Hui Jin, Eric Hsiao-Kuang Wu, Yu-Ting Wang, Chin-Hua Hsu, “An 802.11-based Positioning System for Indoor Applications”, ACTA Press Proceeding (422) Communication Systems and Applications - 2004 [14] Moustafa A. Youssef, Ashok Agrawala, A. Udaya Shankar, “WLAN Location Determination via Clustering and Probability Distributions”, in IEEE PerCom’03. [15] Moustafa Youssef and Ashok Agrawala, “The Horus WLAN Location Determination System” , ACM International Conference On Mobile Systems, Applications And Services Proceedings of the 3rd international conference on Mobile systems, applications, and services. [16] Moustafa Youssef and Ashok Agrawala, “Handling Samples Correlation in the Hours System”, IEEE Infocom2004. [17] Moustafa Youssef, Mohamed Abdallah, Ashok Agrawala, “Multivariate Analysis for Probabilistic WLAN Location Determination Systems”, IEEE MobiQuitous’05. [18] M. berna, B. Lisien, B. Sellner, G. Gordon, F. Pfenning, and S. Thrun, “A learning algorithm for localizing people based on wireless signal strength that uses labeled and unlabeled data”, in IJCAI’03, Acapulco, Mexico, August 2003. [19] Nissanka B. Priyantha, Anit Chakraborty, Hari Balakrishnan, “The Cricket Location-Support system,” Proc. 6th ACM MOBICOM, Boston, MA, August 2000 [20] Nissanka B. Priyantha, Allen Miu, Hari Balakrishnan, Seth Teller, “The Cricket Compass for Context-Aware Mobile Applications”, Proc. 7th ACM MOBICOM, Rome, Italy, July 2001 [21] Paramvir Bahl and Venkata N.Padmanabhan, ”RADAR: An In-Building RF-based User Location and Tracking System”, in IEEE INFOCOM 2000, Mar 2000, pp. 775-784. [22] P. Bahl, A. Balachandran, and V. Padmanabhan , “Enhancements to the RADAR user location and tracking system” ,Technical report, Microsoft Research, February 2000. [23] S. Luhr, H.H. Bui, S. Venkatesh, and G.A. West, “Recognition of human activity through hierarchical stochastic learning”, in First IEEE International Conference on Pervasive Computing and Communications, March 2003. [24] T. Roos, P. Myllymaki, H. Tirri, P. Misikangas, and J. Sievanen, “A probabilistic approach to WLAN user location estimation”, International Journal of Wireless Information Networks, 9(3):155–164, July 2002. [25] Tzu-Chieh Tsai, S-H Kao, and C-L Li, “In-building 802.11b Locating System Based on Wireless Channel Propagation Models”, in 10th Mobile Computing Workshop, 2004. [26] Tzu-Chief Tsai, Cheng-Lin Li, Tsung-Ming Lin, “Reducing Calibration Effort for WLAN Location and Tracking System using Segment Technique”, IEEE AHUC2006. [27] Wenye Wang and Ian F. Akyildiz, “On the Estimation of User Mobility Pattern for Location Tracking in Wireless Networks”, in GLOBECOM 2002, Nov 2002, pp. 619-623. [28] X. Huang et. al., (2001) Spoken Language Processing, Chapter 8 [29] Xiaoyong Chai and Qiang Yang, “Reducing the calibration Effort for Location Estimation Using Unlabeled Samples”, Proceedings of the 3rd IEEE Int’l Conf. on Pervasive Computing and Communications (PerCom 2005).
描述:	碩士國立政治大學資訊科學學系 93753003 94
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0093753003
数据类型:	thesis
显示于类别:	[資訊科學系] 學位論文

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