Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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An Indoor Positioning Method using IEEE 802.11 Channel State Information

  • Escudero, Giovanni (Graduate School of Electronics Engineering, Kyungpook National University) ;
  • Hwang, Jun Gyu (Graduate School of Electronics Engineering, Kyungpook National University) ;
  • Park, Joon Goo (Graduate School of Electronics Engineering, Kyungpook National University)
  • Received : 2016.09.05
  • Accepted : 2017.02.03
  • Published : 2017.05.01
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Abstract

In this paper, we propose an indoor positioning system that makes use of the attenuation model for IEEE 802.11 Channel State Information (CSI) in order to determine its distance from an Access Point (AP) at a fixed position. With the use of CSI, we can mitigate the problems present in the use of Received Signal Strength Indicator (RSSI) data and increase the accuracy of the estimated mobile device's location. For the experiments we performed, we made use of the Intel 5300 Series Network Interface Card (NIC) in order to receive the channel frequency response. The Intel 5300 NIC differs from its counterparts in that it can obtain not only the RSSI but also the CSI between an access point and a mobile device. We can obtain the signal strengths and phases from subcarriers of a system which in turn means making use of this data in the estimation of a mobile device's position.

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References

  1. H. Liu, H. Darabi, P. Banerjee, and J. Liu, "Survey of wireless indoor positioning techniques and systems," IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 37, no. 6. pp. 1067-1080, 2007. https://doi.org/10.1109/TSMCC.2007.905750
  2. X. Wang, S. Mao, S. Pandey, and P. Agrawal, "$CA^2T$: Cooperative Antenna Arrays Technique for pinpoint indoor localization," in Procedia Computer Science, 2014, vol. 34, pp. 392-399. https://doi.org/10.1016/j.procs.2014.07.044
  3. P. Bahl and V. N. Padmanabhan, "RADAR: An Inbuilding RF-based User Location and Tracking System," in Proc. IEEE INFOCOM 2000. The 19th Annual Conference on Computer Communications, 2000, vol. 2, pp. 775-784.
  4. Z. Wu, C. Li, J. Ng, and K. Leung, "Location Estimation via Support Vector Regression," IEEE Trans. Mob. Comput., vol. 6, no. 3, pp. 311-321, 2007. https://doi.org/10.1109/TMC.2007.42
  5. M. A. Youssef and A. Agrawala, "The Horus WLAN location determination system," Proc. 3rd Int. Conf. Mob. Syst. Appl. Serv. - MobiSys '05, pp. 205-218, 2005.
  6. K. Fazel and S. Kaiser, Multi-Carrier and Spread Spectrum Systems: From OFDM and MC-CDMA to LTE and WiMAX: Second Edition. 2008.
  7. Y. Liu, Z. Yang, X. Wang, and L. Jian, "Location, localization, and localizability," J. Comput. Sci. Technol., vol. 25, no. 2, pp. 274-297, 2010. https://doi.org/10.1007/s11390-010-9324-2
  8. M. Brunato and R. Battiti, "Statistical learning theory for location fingerprinting in wireless LANs," Comput. Networks, vol. 47, no. 6, pp. 825-845, 2005. https://doi.org/10.1016/j.comnet.2004.09.004

Cited by

  1. A Novel Passive Indoor Localization Method by Fusion CSI Amplitude and Phase Information vol.19, pp.4, 2019, https://doi.org/10.3390/s19040875
  2. SAPIL: single access point based indoor localisation using Wi-Fi L-shaped antenna array pp.2043-6394, 2019, https://doi.org/10.1049/iet-wss.2018.5129