DOI QR코드

DOI QR Code

Terrain-referenced Underwater Navigation using Rao-Blackwellized Particle Filter

라오-블랙웰라이즈드 입자필터를 이용한 지형참조 수중항법

  • Kim, Taeyun (Ocean Robotics & Intelligence Lab., Division of Ocean Systems Engineering, KAIST) ;
  • Kim, Jinwhan (Ocean Robotics & Intelligence Lab., Division of Ocean Systems Engineering, KAIST) ;
  • Choi, Hyun-Taek (Korea Institute of Ocean Science and Technology)
  • 김태윤 (한국과학기술원 해양시스템공학전공) ;
  • 김진환 (한국과학기술원 해양시스템공학전공) ;
  • 최현택 (한국해양과학기술원)
  • Received : 2013.05.15
  • Accepted : 2013.06.30
  • Published : 2013.08.01

Abstract

Navigation is a crucial capability for all types of manned or unmanned vehicles. However, vehicle navigation in underwater environments still remains a challenging problem since GPS signals for position fixes are not available in the water. Terrain-referenced underwater navigation is an alternative navigation technique that utilizes geometric information of the subsea terrain to correct drift errors due to dead-reckoning or inertial navigation. Terrain-referenced navigation requires the description of an undulating terrain surface as a mathematical function or table, which often leads to a highly nonlinear estimation problem. Recently, PFs (Particle Filters), which do not require any restrictive assumptions about the system dynamics and uncertainty distributions, have been widely used for nonlinear filtering applications. However, PF has considerable computational requirements which used to limit its applicability to problems of relatively low state dimensions. This study proposes the use of a Rao-Blackwellized particle filter that is computationally more efficient than the standard PF for terrain-referenced underwater navigation involving a moderate number of states, and its performance is compared with that of the extended Kalman filter algorithm. The validity and feasibility of the proposed algorithm is demonstrated through numerical simulations.

Keywords

terrain-referenced navigation;nonlinear estimation;rao-blackwellized particle filter;underwater vehicle

Acknowledgement

Supported by : 한국연구재단

References

  1. K. B. Anonsen and O. K. Hagen, "Analysis of real-time terrain aided navigation results from a HUGIN AUV," IEEE Oceans 2010, Seattle, WA, 2010.
  2. J. P. Golden, "Terrain contour matching (TERCOM): A cruise missile guidance aid," SPIE, vol. 238, pp. 10-18, 1980. https://doi.org/10.1117/12.959127
  3. S. M. Lee, Y. M. Yoo, W. H. Lee, D. H. Lee, and C. G. Park, "Performance improvement of TRN batch processing using the slope profile," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 18, no. 4, pp. 384-390, 2012. https://doi.org/10.5302/J.ICROS.2012.18.4.384
  4. I. Nygren, "Terrain navigation for underwater vehicles," Ph.D. dissertation, Royal Institute of Technology, Stockholm, Sweden, 2005.
  5. D. K. Meduna, S. Rock and R. McEwen, "Low-cost terrain relative navigation for long-range AUVs," Proc. of the OCEANS 2008 MTS/IEEE QUEBEC Conference, Quebec City, Canada, 2008.
  6. J. Carlstrom and I. Nygren, "Terrain navigation of the swedish AUV62F vehicle," International Symposium UUST05, Durham, NH, 2005.
  7. O. K. Hagen, K. B. Anonsen and M. Mandt, "The HUGIN realtime terrain navigation system," IEEE Oceans 2010, Seattle, WA, 2010.
  8. J. Kim and T. Kim, "Terrain-based localization using particle filter for underwater navigation," International Journal of Ocean System Engineering, vol. 1, no. 2, pp. 90-95, 2011.
  9. J. W. Langelaan, "State estimation for autonomous flight in cluttered environments," Ph.D. dissertation, Stanford University, pp. 19-21, 2006.
  10. G. Hendeby, R. Karlsson, and F. Gustafsson, "The raoblackwellized particle filter: A filter bank implementation," EURASIP Journal on Advances in Signal Processing, vol. 2010, 2010.
  11. J. Kim, S. S. Vaddi, P. K. Menon, and E. Ohlmeyer, "Comparison between three spiraling ballistic missile state estimators," IEEE Trans. on Aerospace and Electronic Systems, vol. 48, no. 1, pp. 525-541, 2012. https://doi.org/10.1109/TAES.2012.6129653
  12. D. A. Ross, Introduction to Oceanography, NY: HarperCollins College Publishers, New York.
  13. T. Schon and F. Gustafsson, "Marginalized particle filters for mixed linear/nonlinear state-space models," IEEE Transactions on Signal Processing, vol. 53, no. 7, pp. 2279-2289, 2005. https://doi.org/10.1109/TSP.2005.849151

Cited by

  1. A Comparison of Nonlinear Filter Algorithms for Terrain-referenced Underwater Navigation pp.2005-4092, 2018, https://doi.org/10.1007/s12555-017-0504-5