Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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A Performance Comparison of Positioning Methods Considering Measurement Noise

측정 잡음을 고려한 위치 결정 기법의 성능비교

  • 박찬식 (충북대학교 전자정보대학 전자공학부, 컴퓨터정보통신연구소) ;
  • 임재걸 (동국대학교 과학기술대학 컴퓨터멀티미디어학부)
  • Received : 2010.09.10
  • Accepted : 2010.12.01
  • Published : 2010.12.01
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Abstract

This paper proposes three positioning algorithms using TOA measurements: 1) The well-known linearization method using Taylor series, 2) a modified Savarese method considering measurement noise, which does not need linearization, and 3) a modified Bancroft method where TOA measurements instead of pseudorange measurements are considered. Furthermore, through an error analysis, for Savarese method, divergence of altitude is anticipated if the transmitters are located at the same height. To prevent height divergence, the Savarese method is modified again for receivers which assumed moving on the even plane. Error analysis also shows the relationship between Bancroft and Savarese method. From the analysis it is expected that the performance of Savarese method is worse than Bancroft method because of error amplification during difference operation. Experiments using real TOA measurement from the time difference of ultra sound and RF validate the proposed methods and show that analysis is correct.

Keywords

References

  1. B. W. Parkinson, J. J. Spilker Jr., Global Positioning System: Theory and Applications, AIAA, Washington, 1966.
  2. P. Misra and P. Enge, Global Positioning System: Signals, Measurements, and Performance, 2nd Ed., Ganga-Jamuna Press, 2006.
  3. P. Bahl and V. N. Padmanabhan, “RADAR: An in-building RFbased user location and tracking system,” Proc. IEEE Infocom, Tel-Aviv, Israel, vol. 2, pp. 775-784, April 2000.
  4. R. Want, A. Hopper, V. Falcao, and J. Gibbons, “The active badge location system,” ACM Transactions on Information Systems, vol. 10, no. 1, pp. 91-102, Jan. 1992. https://doi.org/10.1145/128756.128759
  5. Ubisense homepage. http://www.ubisense.net/
  6. N. B. Priyantha, The Cricket Indoor Location System, PhD Thesis, Massachusetts Institute of Technology, June 2005.
  7. S. B. Kim, C. S. Park, D.-Y. Kang, H. H. Yun, B.-C. Ahn, E. J. Cha, and S. J. Lee, “An efficient positioning algorithm using ultrasound and RF,” International Journal of Control, Automation, and Systems, vol. 6, no. 4, pp. 544-550, Aug. 2008.
  8. A. Ward, A. Jones, and A. Hopper, “A new location technique for the active office,” IEEE Personal Communications, vol. 4, no. 5, pp. 42-47, Oct. 1997. https://doi.org/10.1109/98.626982
  9. S. B. Kim, C. Park, H. H. Yun, D.-Y. Kang, E. J. Cha, and S. J. Lee, “Positioning algorithm reducing blundered error in wireless sensor networks (in Korean),” Proc. of 14th GNSS Workshop, Jeju, Korea, Nov. 2007.
  10. C. Savarese, K. Langendoen, and J. Rabaey, “Robust positioning algorithms for distributed ad-hoc wireless sensor networks,” USENIX Technical Annual Conference, pp. 317-328, Monterey, CA, 2002.
  11. S. Bancroft, “An algebraic solution of the GPS equation,” IEEE transaction on Aerospace and Electronic System, vol. AES-21, no. 7, pp. 56-59, 1985. https://doi.org/10.1109/TAES.1985.310538
  12. Hyvarinen, A., J. Karhunen, E. Oja, Independent Component Analysis, John Wiley and Sons, 2001.
  13. G. H. Golub and van Loan, C. F.: Matrix computations, The Johns Hopkins University Press, Baltimore, Maryland, USA, 2nd Ed., 1998.

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