Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Improvement of Position Estimation Based on the Multisensor Fusion in Underwater Unmanned Vehicles

다중센서 융합 기반 무인잠수정 위치추정 개선

  • 이경수 (국방대학교 국방정보체계학과) ;
  • 윤희병 (국방대학교 국방정보체계학과)
  • Received : 2010.09.21
  • Accepted : 2011.04.15
  • Published : 2011.04.25
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose the position estimation algorithm based on the multisensor fusion using equalization of state variables and feedback structure. First, the state variables measured from INS of main sensor with large error and DVL of assistance sensor with small error are measured before prediction phase. Next, the equalized state variables are entered to each filter and fused the enhanced state variables for prediction and update phases. Finally, the fused state variables are returned to the main sensor for improving the position estimation of UUV. For evaluation, we create the moving course of UUV by simulation and confirm the performance of position estimation by applying the proposed algorithm. The evaluation results show that the proposed algorithm is the best for position estimation and also possible for robust position estimation at the change period of moving courses.

본 논문은 상태변수 평준화 및 되먹임구조를 이용하여 무인잠수정의 위치추정을 개선하기 위한 다중센서 융합 기반의 위치추정 알고리즘을 제안한다. 이를 위해 먼저 상대적으로 오차가 큰 주 센서인 INS와 오차가 작은 보조센서인 DVL에서 측정되는 상태변수를 예측단계 이전에 융합하여 상태변수 평준화 과정을 수행한다. 그 다음, 평준화된 상태변수를 각 필터에 입력하여 예측 및 수정단계의 칼만 필터링 과정을 통해 최종 수정된 상태변수를 융합시키며, 마지막으로 이를 다시 주센서에 되먹임함으로서 무인잠수정의 위치추정을 개선한다. 평가를 위해 무인잠수정의 기동모델에 대한 시뮬레이션을 실시하여 기동경로를 생성하고 제안 알고리즘을 적용하여 위치추정 성능을 확인한다. 평가 결과, 제안 알고리즘이 다중센서 융합 알고리즘 중 가장 우수한 위치추정 성능을 보였으며, 또한 기동침로가 변경되는 구간에서도 강인한 위치추정이 가능하다는 것이 증명되었다.

Keywords

References

  1. M.B. Larsen, “High performance Autonomous Underwater Navigation-Experimental Results,” GITC(Article Reprint), vol. 6, no. 1, pp. 1-5, Feb. 2002.
  2. J.C. Kinsey and L.L. Whitcomb, “Towards In-Situ Calibration of Gyro and Doppler Navigation Sensors for Precision Underwater Vehicle Navigation,” IEEE international conference on Robotics and Automation, pp. 4016-4023, May 2002.
  3. P.M. Lee, S.M. Kim, B.H. Jeon, H.T. Choi, and C.M. Lee, “Improvement on an Inertial-Doppler Navigation System of Unmanned Underwater Vehicles Using a complementary Range Sonar,” International Symposium on Underwater Technology, pp. 133-138, April, 2004.
  4. C.M. Rogers, “Methods of Multisensor Tracking,” London Communications Symposium, pp. 1-4, 2003.
  5. U.S Navy, The Navy Unmanned Undersea Vehicle(UUV) Master Plan, pp. 39-42, Nov. 2004.
  6. D. Hyun, H. Yoon, “A Fusion Algorithm Considering Error Characteristics of the Multi-sensor,” Journal of KIISE: Computer Systems and Theory, vol. 36, no. 4, pp. 274-282, Aug. 2009.
  7. S. Lee and H. Yoon, “GPS/INS Data Fusion and Localization using Fuzzy Inference/UPF,” KIIS, vol. 19, no. 3, pp. 408-414, June 2009. https://doi.org/10.5391/JKIIS.2009.19.3.408
  8. X. Rong Li and V.P. Jklkov, “Survey of Maneuvering Target Tracking,” IEEE Transactions On Aerospace and Electronic Systems, vol. 39, no. 4, pp. 1255-1321, Oct. 2003.
  9. T. Kirubarajan and Y. Bar-shalom, “Kalman Filter Versus IMM Estimator: When Do We Need the Latter?,” IEEE Transactions On Aerospace and Electronic Systems, vol. 39, no. 4, pp. 1452-1457, Oct. 2003. https://doi.org/10.1109/TAES.2003.1261143
  10. K. Gade, Introduction to Inertial Navigation and Kalman filtering, INS Tutorial, Norwegian Space Center, p. 19, June 2008.
  11. A.M. Plotink and S.M. Rock, “A Multi-sensor Approach to Automatic Tracking of Midwater Targets by an ROV,” AIAA Guidance, Navigation and Control Conference, pp. 1-10, Aug. 2005.
  12. G. Welch, G. Bishop, An introduction to the Kalman Filter, UNC-Chapel Hill, pp. 4-12, July 2006.
  13. M.G Song and Y. Park, “Multi-sensor Data Fusion Methods Based on Kalman Filter for Attitude and Vibration Control of The Biped Robot,” Journal of the Korean Society for Precision Engineering, vol. 25, no. 4, pp. 39-46, April 2008.