Design of GPS-aided Dead Reckoning Algorithm of AUV using Extended Kalman Filter

확장칼만필터를 이용한 무인잠수정의 GPS 보조 추측항법 알고리즘 설계

  • Kang, Hyeon-Seok (Department of Convergence Study on the Ocean Science and Technology, Ocean Science and Technology School, KMOU-KIOST) ;
  • Hong, Sung-Min (Department of Convergence Study on the Ocean Science and Technology, Ocean Science and Technology School, KMOU-KIOST) ;
  • Sur, Joo-No (Research Institute of Industrial Technology, KMOU) ;
  • Kim, Joon-Young (Department of Mechanical Engineering, KMOU)
  • 강현석 (한국해양대학교-한국해양과학기술원 해양과학기술전문대학원 해양과학기술융합학과) ;
  • 홍승민 (한국해양대학교-한국해양과학기술원 해양과학기술전문대학원 해양과학기술융합학과) ;
  • 서주노 (한국해양대학교 산업기술연구소) ;
  • 김준영 (한국해양대학교 기계공학부)
  • Received : 2016.11.08
  • Accepted : 2017.01.10
  • Published : 2017.02.28


This paper introduces a GPS-aided dead reckoning algorithm that asymptotically estimates the heading bias error of a magnetic compass based on geodetic north, improves the position error accumulated by dead reckoning, and helps the estimated position of an AUV to represent a position in the NED coordinate system, by receiving GPS position information when surfaced. Based on the results of a simulation, the locational error was bounded with a modest distance, after estimating the AUV position and heading bias error of the magnetic compass when surfaced. In other words, it was verified that proposed algorithm improves the position error in the NED coordinate system.


Supported by : 한국연구재단, 한국항공우주연구원


  1. Lee, C.-M., Lee, P.-M., Seong, W.-J., 2003. Underwater Hybrid Navigation Algorithm Based on an Inertial Sensor and a Doppler Velocity Log Using an Indirect Feedback Kalman Filter. Journal of Ocean Engineering and Technology, 17(6), 83-90.
  2. Lee, P.-M., Jeon, B.-H., Kim, S.-M., Lee, C.-M., Lim, Y.-K., Yang, S.-I., 2004. A Hybrid Navigation System for Underwater Unmanned Vehicles, Using a Range Sonar. Journal of Ocean Engineering and Technology, 18(4), 33-39.
  3. Park, Y.-S., Lee, D.-H., Choi, W.-S., Lee, J.-M., 2015. Sea-surface Localization of AUV using Extended Kalman filter for INS/GPS, Conference of Institute of Control. Robotics and Systems, 97-98.
  4. Paull, L., Saeedi, S., Seto, M., Li, H., 2014. AUV Navigation and Localization: A Review. IEEE Journal of Oceanic Engineering, 39(1), 131-149.
  5. The Society of Naval Architects and Marine Engineers (SNAME), 1950. Nomenclature for Treating the Motion of a Submerged Body Through a Fluid. Technical and Research Bulletin, No. 1-5.
  6. Yoo, T.-S., Chung G.-P., Yoon, S.-I., 2013. Development of Integrated Navigation Algorithm for Underwater Vehicle using Velocity Filter. Journal of Ocean Engineering and Technology, 27(2), 93-99.
  7. Boncal, R.J., 1987. A Study of Model Based Maneuvering Controls for Autonomous Underwater Vehicles. M.E. Thesis, Naval Postgraduate School, Monterey, CA.
  8. Choi, W.-S., Hoang, N.-M., Jung, J.-H., Lee, J.-M., 2014. Navigation System Development of the Underwater Vehicles Using the GPS/INS Sensor Fusion. 7th International Conference of Intelligent Robotics and Applications, 491-497.
  9. Fossen, T.I., 1994. Guidance and Control of Ocean Vehicles. John Wiley & Sons.
  10. Fossen, T.I., 2011. Handbook of Marine Craft Hydrodynamics and Motion Control. John Wiley & Sons.
  11. Gertler, M., Hagen, G.R., 1967. Standard Equations of Motion for Submarine Simulations. NSRDC Report 2510.
  12. Healey, A.J., Lienard, D., 1993. Multivariable Sliding Mode Control for Autonomous Diving and Steering of Unmanned Underwater Vehicles. IEEE Journal of Oceanic Engineering, 18(3), 327-339.

Cited by

  1. Navigation System for a Deep-sea ROV Fusing USBL, DVL, and Heading Measurements vol.31, pp.4, 2017,