DOI QR코드

DOI QR Code

AP Selection Criteria for UAV High-precision Indoor Positioning based on IEEE 802.11 RSSI Measurement

IEEE 802.11 RSSI 기반 무인비행로봇 실내측위를 위한 AP 선택 기법

  • Hwang, Jun Gyu (Department of Mobile Communication, Kyungpook National University) ;
  • Park, Joon Goo (Kyungpook National University, School of Electronics Engineering)
  • 황준규 (경북대학교 모바일통신공학과) ;
  • 박준구 (경북대학교 전자공학과)
  • Received : 2014.08.30
  • Accepted : 2014.09.22
  • Published : 2014.12.01

Abstract

As required performance of UAV (Unmanned Aerial Vehicle) becomes more complex and complicated, required positioning accuracy is becoming more and more higher. GPS is a reliable world wide positioning providing system. Therefore, UAV generally acquires position information from GPS. But when GPS is not available such as too weak signal or too less GPS satellites environments, UAV needs alternative positioning system such as network positioning system. RSSI (Received Signal Strength Indicator) based positioning, which is one method of network positioning technologies, determines its position using RSSI measurements containing distance information from AP (Access Point)s. In that method, a selected AP's configuration has strong and tight relationship with its positioning errors. In this paper, for, we additionally account AP's configuration information by adopting DOP (Dilution of Precision) into AP selection procedures and provide more accurate RSSI based positioning results.

Keywords

References

  1. A. Nafarieh and J. Ilow, "A testbed for localizing wireless LAN devices using received signal strength," IEEE CNSRC, pp. 481-487, 2008.
  2. E.-J. Zhong and T.-Z. Huang, "Geometric dilution of precision in navigation computation," IEEE MLC, pp 4116-4119, 2006.
  3. G. Lachapelle, "GNSS indoor location technologies," Journal of Global Positioning Systems, vol. 3, no. 1-2, pp. 2-11, 2004. https://doi.org/10.5081/jgps.3.1.2
  4. P. Bahl and V. N. Padmanabhan, "An in-building RF-based user location and tracking system," INFOCOM, vol. 2, pp. 775-784, 2000.
  5. C.-B. Lim, S.-H. Kang, H.-H. Cho, S.-W. Park, and J.-G. Park, "An enhanced indoor localization algorithm based on IEEE 802.11 WLAN using rssi and multiple parameters," ICSNC, pp. 238-242, 2010.
  6. R. Want, A. Hopper, V. Falcao, and J. Gibbons, "The active badge location system," ACM Transactions on Information Systems, vol. 10 pp. 91-102, 1992. https://doi.org/10.1145/128756.128759
  7. B. B. Parodi, H. Lenz, A. Szabo, H. Wang, J. Horn, J. Bamberger, and J. Obradovic, "Initialization and online-learning of RSS maps for indoor/campus localization," IEEE/ION PLANS 2006, San Diego, USA, pp. 164-172, 2006.
  8. M. Vossiek, L. Wiebking, P. Gulden, J. Wieghardt, and C. Hoffmann, "Wireless local positioning-concepts, solutions, application," IEEE Microwave Magazine, vol. 4, pp. 77-87, 2003. https://doi.org/10.1109/MMW.2003.1266069
  9. H. Wang, H. Lenz, A. Szabo, J. Bamberger, and U. D. Hanebeck, "WLAN-based pedestrian tracking using particle filters and low-cost MEMS sensors," Workshop on Positioning, Navigation and Communication, Hannover, Germany, 2007.
  10. P. Krishnamurthy, K. Pahlavan, and J. Beneat, "Wide band Radio Propagation modeling for indoor geolocation applications," IEEE Communications Magazine, vol. 36, no. 4, pp. 60-85, 1998.
  11. P. Bahl and V. N. Padmanabhan, "RADAR: An in-buildingRF-based user location and tracking system," IEEE Infocom2000, Tel Aviv, Israel, vol. 2, pp. 775-784, 2000.
  12. N. B. Priyantha, A. Chakraborty, and H. Balakrishnan, "The cricket location-support system," Proc. the 6th Annual International Conference on Mobile Computing and Networking, Boston, Massachusetts, United States, pp. 32-43, 2000.
  13. A S. Kim, J. Hwang, and J. Park. "Enhanced indoor positioning algorithm using WLAN RSSI measurements considering the relative position information of AP configuration," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 19, no. 2, pp. 146-151, 2013. https://doi.org/10.5302/J.ICROS.2013.19.2.146