Top > JRM > Probabilistic Localization of Mobile Wireless LAN Client in Mu ...

single-rb.php

JRM Vol.23 No.4 pp. 475-483
doi: 10.20965/jrm.2011.p0475
(2011)

Paper:

Views over last 60 days: 556

Probabilistic Localization of Mobile Wireless LAN Client in Multistory Building Based on Sparse Bayesian Learning

Tomohiro Umetani*, Tomoya Yamashita**, and Yuichi Tamura*

*Department of Intelligence and Informatics, Konan University, 8-9-1 Okamoto, Higashinada, Kobe, Hyogo 658-8501, Japan

**Graduate School of Natural Science, Konan University

Received:
January 11, 2011
Accepted:
April 27, 2011
Published:
August 20, 2011
Creative Commons License
Keywords:
localization, wireless LAN, multistory building, probabilistic model, sparse Bayesian learning
Abstract
This paper describes a method for localizing wireless mobile clients in a multistory building using a public wireless Local Area Network (LAN) system. Physical location data on personal devices and mobile robots is important to information services and robot applications. Wireless mobile clients are localized in a multistory building using public wireless LAN access points placed, three-dimensionally in the building. Information on the floor number and client location is acquired probabilistically, with estimation providing a probabilistic model for localization based on sparse Bayesian learning. Results of experiments confirm the feasibility of our proposal.
Cite this article as:
T. Umetani, T. Yamashita, and Y. Tamura, “Probabilistic Localization of Mobile Wireless LAN Client in Multistory Building Based on Sparse Bayesian Learning,” J. Robot. Mechatron., Vol.23 No.4, pp. 475-483, 2011.
Data files:
References
  1. [1] J. H. Lee and H. Hashimoto, “Intelligent Space – concept and contents,” Advanced Robotics, Vol.16, No.3, pp. 265-280, 2002.
  2. [2] J. Hightower and G. Borrieello, “Location Systems for Ubiquitous Computing,” IEEE Computer, Vol.34, No.8, pp. 57-66, 2001.
  3. [3] T. Manesis and N. Avouris, “Survey of position location techniques in mobile systems,” in: Proc. 7th Int. Conf. on Human Computer Interaction with Mobile Devices & Services, pp. 291-294, 2005.
  4. [4] M. E. Tipping, “Sparse Bayesian Learning and the Relevance Vector Machine,” J. ofMachine Learning Research, Vol.1, pp. 211-244, 2001.
  5. [5] H. Zhang and J.Malik, “Selecting Shape Features UsingMulti-class Relevance Vector Machine,” Technical Report No.UCB/EECS-2005-6, University of California at Berkeley, 2005.
  6. [6] H. Liu, H. Darabi, P. Banerjee, and J. Liu, “A Survey of Wireless Indoor Positioning Techniques and Systems,” IEEE Trans. Systems, Man, and Cybernetics – Part C: Applications and Reviews, Vol.37, No.6, pp. 1067-1080, 2007.
  7. [7] P. Enge and P. Misra, “Special Issue on GPS: The Global Positioning System,” Proc. IEEE, Vol.87, No.1, pp. 3-15, 1999.
  8. [8] A. Georgiev and P. K. Allen, “Localization Methods for a Mobile Robot in Urban Environments,” IEEE Trans. Robotics, Vol.20, No.5, pp. 851-864, 2004.
  9. [9] K. Kodaka, H. Niwa, and S. Sugano, “Active Localization of a Robot on a Lattice of RFID Tags by Using an Entropy Map,” in: Proc. 2009 IEEE Int. Conf. on Robotics and Automation, pp. 3921-3927, 2009.
  10. [10] D. Joho, C. Plagemann, and W. Burgard, “Modeling RFID Signal Strength and Tag Detection for Localization and Mapping,” in: Proc. 2009 IEEE Int. Conf. on Robotics and Automation, pp. 3160-3165, 2009.
  11. [11] H. Ahn and W. Yu, “Environmental-Adaptive RSSI-Based Indoor Localization,” IEEE Trans. Automation Science and Engineering, Vol.6, No.4, pp. 626-632, 2009.
  12. [12] J. Rekimoto, T. Miyaki, and T. Ishizawa, “LifeTag: WiFi-based Continuous Location Logging for Life Pattern Analysis,” in: Proc. 3rd Int. Symposium on Location- and Context-Awareness (LoCA2007), pp. 35-49, 2007.
  13. [13] S. Ito and N. Kawaguchi, “Bayesian Based Location Estimation System Using Wireless LAN,” in: Proc. 3rd Int. Conf. on Pervasive Computing and Communication Workshop (PerCom 2005 Workshops), pp. 273-278, 2005.
  14. [14] N. Kawaguchi, “WiFi Location Information System for Both Indoors and Outdoors,” Lecture Notes In Computer Science, Vol.5518, pp. 638-645, 2009.
  15. [15] P. Bahl and V. N. Padmanabhan, “RADAR: An In-Building RFBased User Location and Tracking System,” in: Proc. IEEE Infocom 2000, Vol.2, pp. 775-784, 2000.
  16. [16] P. Bahl and V. N. Padmanabhan, “Enhancements to the RADAR User Location and Tracking System,” Technical Report MSR-TR-2000-12, Microsoft Research, 2000.
  17. [17] A. M. Ladd, K. E. Bekris, A. P. Rudys, D. S. Wallach, and L. E. Kavraki, “On the Feasibility of Using Wireless Ethernet for Indoor Localization,” IEEE Trans. Robotics and Automation, Vol.20, No.3, pp. 555-559, 2004.
  18. [18] A. M. Ladd, K. E. Bekris, A. P. Rudys, L. E. Kavraki, and D. S. Wallach, “Robotics-Based Location Sensing Using Wireless Ethernet,” Wireless Networks, Vol.11, No.1-2, pp. 189-204, 2005.
  19. [19] M. N. Borenović and A. M. Nešković, “Positioning in WLAN environment by use of artificial neural networks and space partitioning,” Annals of Telecommunications, Vol.64, No.9-10, pp. 665-676, 2009.
  20. [20] S. Thrun, W. Burgard, and D. Fox, “Probabilistic Robotics,” MIT Press, 2005.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

Copyright© 2011 by Fuji Technology Press Ltd. and Japan Society of Mechanical Engineers. All right reserved.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 18, 2024