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標題: 一個依環境變化自動產生虛擬訓練資料之室內無線區域網路定位模型
An Adaptive Indoor Wireless LAN Locating Model Using Virtual Training Data Generated by Environmental Changes
作者: 呂加祥
Lyu, Jia-Shang
關鍵字: 無線區域網路;室內定位;虛擬訓練資料;訊號強度(RSS);Access Point
出版社: 資訊科學與工程學系所
引用: [1] P. Bahl and V. N. Padmanabhan, "RADAR: An in-building RF-based user location and tracking system," in INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, pp. 775-784, 2000. [2] M. Ciurana, F. Barcelo, and S. Cugno, "Indoor tracking in WLAN location with TOA measurements," in MobiWac ''06 Proceedings of the 4th ACM international workshop on Mobility management and wireless access, pp. 121-125, 2006. [3] Cybernet, "Firefly Motion Capture System," [4] R. Hansen, R. Wind, C. S. Jensen, and B. Thomsen, "Algorithmic strategies for adapting to environmental changes in 802.11 location fingerprinting," in Indoor Positioning and Indoor Navigation (IPIN), pp. 1-10, 2010. [5] J. Hightower, R. Want, and G. Borriello, "SpotON: An indoor 3D location sensing technology based on RF signal strength," UW CSE 00-02-02, University of Washington, Department of Computer Science and Engineering, Seattle, WA, 2000. [6] K. F. Kao, I. E. Liao, and J. S. Lyu, "An indoor location-based service using access points as signal strength data collectors," in Indoor Positioning and Indoor Navigation (IPIN), pp. 1-6, 2010. [7] Kismet, "What is Kismet?," [8] H. Lim, L. C. Kung, J. C. Hou, and H. Luo, "Zero-configuration indoor localization over IEEE 802.11 wireless infrastructure," Wireless Networks, vol. 16, pp. 405-420, 2010. [9] H. Lim, L. C. Kung, J. C. Hou, and H. Luo, "Zero-configuration, robust indoor localization: Theory and experimentation," work, vol. 2005, p. 1818, 2005. [10] L. F. M. d. Moraes and B. A. A. Nunes, "Calibration-free WLAN location system based on dynamic mapping of signal strength," in MobiWac ''06 Proceedings of the 4th ACM international workshop on Mobility management and wireless access pp. 92-99, 2006. [11] L. M. Ni, Y. Liu, Y. C. Lau, and A. P. Patil, "LANDMARC: indoor location sensing using active RFID," Wireless Networks, vol. 10, pp. 701-710, 2004. [12] D. Niculescu and B. Nath, "Ad hoc positioning system (APS) using AOA," in INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. IEEE Societies, pp. 1734-1743 vol. 3, 2003. [13] OpenWrt, "OpenWRT: A Linux distribution for WRT54G," [14] R. J. Orr and G. D. Abowd, "The smart floor: A mechanism for natural user identification and tracking," in CHI EA ''00 extended abstracts on Human factors in computing systems, pp. 275-276, 2000. [15] N. B. Priyantha, A. Chakraborty, and H. Balakrishnan, "The cricket location-support system," pp. 32-43, 2000. [16] StreamSpin, "StreamSpin : A New and Innovative Platform for Delivering and Recieving Mobile Services," [17] N. Swangmuang and P. Krishnamurthy, "An effective location fingerprint model for wireless indoor localization," Pervasive and Mobile Computing, vol. 4, pp. 836-850, 2008. [18] R. Want, A. Hopper, V. Falcao, and J. Gibbons, "The active badge location system," ACM Transactions on Information Systems (TOIS), vol. 10, pp. 91-102, 1992. [19] A. Ward, A. Jones, and A. Hopper, "A new location technique for the active office," Personal Communications, IEEE, vol. 4, pp. 42-47, 2002. [20] J. Yin, Q. Yang, and L. Ni, "Adaptive temporal radio maps for indoor location estimation," in Pervasive Computing and Communications, pp. 85-94, 2005.
室內無線區域網路(WLAN)定位系統的基本原理,在於使用定位時接收到的訊號強度(Received signal strengths ,RSS),與訓練時定位點收到的訊號強度做比較,找出訊號強度最相似的做為判斷位置的依據。然而,由於同一位置上量測到的訊號強度,很容易因為環境的改變,像是有人走動、空氣濕度等等因素而使得訊號強度產生變化進而導致定位上的誤差。
本研究提出一個依照環境變化自動產生虛擬訓練資料的無線網路定位模型,在本模型中,AP除了會蒐集行動裝置的訊號強度,也會蒐集其它AP的訊號強度。在實際定位時,系統會比較離線階段(Off-line phase)與上線定位階段(On-line phase)AP間彼此量測到的訊號強度強弱,作為調整大尺度傳播模型(Large-Scale Propagation Model)中路徑衰退係數(Path loss exponent)的依據,然後藉由調整過後的路徑衰退係數配合大尺度傳播模型去計算出各個定位點的虛擬訊號資料,使得產生的虛擬訓練資料更符合當下的環境。

The principle of indoor positioning system using Wi-Fi is to compare the received signal strength (RSS) in testing phase with the RSS collected in training phase and then predict the position based on the RSS model built in the training phase. However, the RSS is so sensitive that it's easily influenced by the environmental changes.
In this thesis, we proposed a Wi-Fi localization model which could adapt the environmental changes and generate the virtual training data. In this localization model, the APs not only record the RSS of mobile devices but also record the RSS between APs. While doing localization, the system will compare the RSS in training phase against the RSS between APs in testing phase. According to the results, the system will adjust the path loss exponent in the large-scale propagation model and then produce the virtual training data which can best describe the environmental changes.
By using the proposed model, we can improve the localization accuracy and save the time spent on collecting training data. Furthermore, the system uses APs as the RSS collectors, which makes the system much easier to be deployed.
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