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Estimated Position of Sea-Surface Beacon Using DWT/UKF

DWT/UKF를 이용한 수면 BEACON의 위치추정

  • 윤바다 (부산대학교 전자전기공학부) ;
  • 윤하늘 (부산대학교 전자전기공학부) ;
  • 최성희 (부산대학교 전자전기공학부) ;
  • 이장명 (부산대학교 전자전기공학부)
  • Received : 2012.09.12
  • Accepted : 2013.01.29
  • Published : 2013.04.01

Abstract

A location estimation algorithm based on the sea-surface beacon is proposed in this paper. The beacon is utilized to provide ultrasonic signals to the underwater vehicles around the beacon to estimate precise position of underwater vehicles (ROV, AUV, Diver robot), which is named as USBL (Ultra Short Baseline) system. It utilizes GPS and INS data for estimating its position and adopts DWT (Discrete Wavelet Transform) de-noising filter and UKF (Unscented KALMAN Filter) elaborating the position estimation. The beacon system aims at estimating the precise position of underwater vehicle by using USBL to receive the tracking signals. The most important one for the precise position estimation of underwater vehicle is estimating the position of the beacon system precisely. Since the beacon is on the sea-waves, the received GPS signals are noisy and unstable most of times. Therefore, the INS data (gyroscope sensor, accelerometer, magnetic compass) are obtained at the beacon on the sea-surface to compensate for the inaccuracy of the GPS data. The noises in the acceleration data from INS data are reduced by using DWT de-noising filter in this research. Finally the UKF localization system is proposed in this paper and the system performance is verified by real experiments.

Keywords

References

  1. S. H. Lee and D. H. Yoon, "Wavelet transform (Well Defined)," 2nd Ed., JinHan Books (in Korean), 8984321109, 2003.
  2. J. Opderbecke, "At-sea calibration of a USBL underwater vehicle positioning system," OCEANS '97. MTS/IEEE Conference Proceedings, vol. 1, pp. 721-726, Oct. 1997.
  3. P. Rigby, O. Pizarro, and S. B. Williams, "Towards georeferenced AUV navigation through fusion of USBL and DVL measurements," OCEANS 2006. MTS/IEEE Conference Proceedings, vol. 1, pp. 721-726, Oct. 1997.
  4. M. Morgado, P. Oliveira, C. Silvestre, and J. F. Vasconcelos, "USBL/INS tightly-coupled integration technique for underwater vehicles," Information Fusion, 2006 9th International Conference, vol. 1, pp. 1-8, Jul. 2006.
  5. G. T. Schmidt, "INS/GPS technology trends," NATO Research and Technology Organization, pp. 1-16, May 2009.
  6. K. N. Shaikh, A. R. bin M. Shariff, H. Jamaluddin, and S. Mansoor, "GPS-Aided-INS for mobile mapping in precision agriculture," Map Asia Conference, 2003.
  7. K. J. Kim, C. G. Park, M. J. Yu, and Y. B. Park "A performance comparison of extended and unscented Kalman filters for INS/GPS tightly coupled approach," Journal of Control, Automation, and Systems Engineering, vol. 12, no. 8, Aug. 2007. https://doi.org/10.5302/J.ICROS.2006.12.8.780
  8. H. B. Kang, D. K. Kin, and J. K. Seo, "Wavelet theory and its applications," DaeWoo 509 (in Korean), 2001.
  9. A. K. Chan and C. Peng, "Wavelets for sensing technologies," Artech House Publishers, 2003.
  10. H.-S. Choi, H.-I. Park, M.-S. Roh, and M.-O. So "A sliding mode control of an underwater robotic vehicle under the influence of thrust dynamics," Journal of the Korean Society of Marine Engineering, vol. 33, no. 8, pp. 1203-1211, Nov. 2009. https://doi.org/10.5916/jkosme.2009.33.8.1203
  11. W. K. Seo and J. M. Lee, "Precise outdoor localization of a GPS-INS integration system using discrete wavelet transforms and unscented particle filter," The institute of Electronics Engineers of Korea (in Korean), vol. 48, no. 6, pp. 82-90, Nov. 2011.
  12. S. Y. Hwang and J. M. Lee, "Estimation of attitude and position 348 of moving objects using multi-filtered inertial navigation system," The institute of Electronics Engineers of Korea (in Korean), vol. 60, no. 12, pp. 2383-2396, Dec. 2011.
  13. Y. K. Kim, B. Y. Hyeon, Y. W. Cho, and K. S. Seo, "Robust tracking algorithm for moving object using Kalman filter and variable search window technique," Institute of Control, Robotics and Systems (in Korean), vol. 18, no. 7, pp. 673-679, Jul. 2012. https://doi.org/10.5302/J.ICROS.2012.18.7.673
  14. H. C. Cho and G. H. Kim, "Learning of differential neural networks based on Kalman-Bucy filter theory," Institute of Control, Robotics and Systems (in Korean), vol. 17, no. 8, pp. 777-782, Aug. 2011. https://doi.org/10.5302/J.ICROS.2011.17.8.777