政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/32673

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/32673

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政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/32673

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/32673

Title:	利用動態訊號資料庫以減少測量數之無線網路定位系統 Reducing Calibration Effort for WLAN Locating System with Dynamic Radio Map
Authors:	簡盧德 Chien Lu,Te
Contributors:	蔡子傑 Tsai,Tzu-Chief 簡盧德 Chien Lu,Te
Keywords:	定位無線網路訊號強度機器學習資料庫 locating WLAN signal strength learning radio map
Date:	2007
Issue Date:	2009-09-17 14:01:46 (UTC+8)
Abstract:	隨著無線網路的興起，許多相關的研究議題也應運而生，利用無線網路(WLAN)對於使用者位置的判斷與追蹤就是其中相當熱門的一塊。經過近幾年的發展，室內WLAN定位誤差的進步空間已達到極限，其原因主要出在無線訊號傳播的物理性質所產生的侷限。然而，大部分擁有良好精準度的定位系統是建立在不切實際的人力成本上，故我們將著眼點放在如何減少收集大量訊號所耗費的人力，同時保持不錯的精準度。取得AP位置所消耗的人力資源也是我們考慮的一環。因此，我們提出一套新的定位系統：首先建立少數的資料點，再透過推測基地台位置和插入機制來完成訊號資料庫的初步建置。然後在定位的同時收集使用者接收到連續的訊號強度，透過隱馬可夫鏈建立的模型，再配合其他演算法來更新訊號資料庫。實驗結果顯示，相較於其他兩個定位系統，我們的系統能夠減少最多的人力建置資源，並且達到有競爭力的定位精準度。除此之外，我們也分析了系統在使用舊的資料庫或是不同的實驗環境下，能夠展現怎樣的定位結果。 Following the raise of Wireless LAN networks, there are a lot of relative research issues in today’s life. Tracking and locating mobile users in RF-based WLAN (IEEE 802.11) is a very important issue in location-based applications area. The error distances of indoor WLAN locating was decreased to approximately 1.5 meter in recent years. However, the improvement in accuracy was limited due to the nature of radio propagation. Many researches which contain precise accuracy were based on an impractical effort of collecting too much signal data which we usually called “calibration” in this area. So this thesis focuses on how to reduce the calibration efforts without losing too much accuracy. Confirming the allocation of access points is another kind of calibration effort we concerned. As a consequence, we proposed a new locating system: first we calibrated few points and utilized inferring AP’s position and interpolation to complete radio map. During location estimation phase, radio map could be updated dynamically using learning mechanism modeled by HMM and other algorithms. In the experimental results, we proved our system maintained a comparable accuracy under reducing much calibration effort than other two locating systems. Besides, we analyzed the performance of our system with elder radio map and in two different experimental environments.
Reference:	[1] 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. [2] P. Bahl, A. Balachandran, and V. Padmanabhan , “Enhancements to the RADAR user location and tracking system” ,Technical report, Microsoft Research, February 2000. [3] 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. [4] 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). [5] 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. [6] Ankur Agiwal, Parakram Khandpur, Huzur Saran, “LOCATOR - Location Estimation System For Wireless LANs”, WMASH’04, October 1, 2004, Philadelphia, Pennsylvania, USA. [7] 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. [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] Ming-Hui Jin, Eric Hsiao-Kuang Wu, Yu-Ting Wang, Chin-Hua Hsu, “802.11-based Positioning System for Context Aware Applications”, Globecom2003. [10] 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 [11] Moustafa A. Youssef, Ashok Agrawala, A. Udaya Shankar, “WLAN Location Determination via Clustering and Probability Distributions”, in IEEE PerCom’03. [12] 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. [13] Moustafa Youssef and Ashok Agrawala, “Handling Samples Correlation in the Hours System”, IEEE Infocom2004. [14] Moustafa Youssef, Mohamed Abdallah, Ashok Agrawala, “Multivariate Analysis for Probabilistic WLAN Location Determination Systems”, IEEE MobiQuitous’05. [15] 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. [16] 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. [17] Tzu-Chief Tsai, Cheng-Lin Li, Tsung-Ming Lin, “Reducing Calibration Effort for WLAN Location and Tracking System using Segment Technique”, IEEE AHUC2006. [18] 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. [19] 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). [20] Nissanka B. Priyantha, Anit Chakraborty, Hari Balakrishnan, “The Cricket Location-Support system,” Proc. 6th ACM MOBICOM, Boston, MA, August 2000 [21] 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 [22] 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. [23] X. Huang et. al., (2001) Spoken Language Processing, Chapter 8 [24] Tzu-Chieh Tsai, Cheng-Lin Li, “Reducing Calibration Effort for WLAN Location System Using Segment Technique with Autocorrelation”, in IEEE ChinaCOMM, October 2006. [25] 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 [26] L. R. Rabinerand B. H. Juang, (1993) Fundamentals of Speech Recognition, Chapter 6 [27] 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.
Description:	碩士國立政治大學資訊科學學系 94753002 96
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0094753002
Data Type:	thesis
Appears in Collections:	[Department of Computer Science ] Theses

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