Construction Method of the Topographical Features Model for Underwater Terrain Navigation

Open access

Abstract

Terrain database is the reference basic for autonomous underwater vehicle (AUV) to implement underwater terrain navigation (UTN) functions, and is the important part of building topographical features model for UTN. To investigate the feasibility and correlation of a variety of terrain parameters as terrain navigation information metrics, this paper described and analyzed the underwater terrain features and topography parameters calculation method. Proposing a comprehensive evaluation method for terrain navigation information, and constructing an underwater navigation information analysis model, which is associated with topographic features. Simulation results show that the underwater terrain features, are associated with UTN information directly or indirectly, also affect the terrain matching capture probability and the positioning accuracy directly.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • 1. Anonsen K.B. and Hagen O.K. An analysis of real-time terrain aided navigation results from a HUGIN AUV OCEANS Vol. 10 pp. 1-9 2010.

  • 2. Anonsen K.B. and Hagen O.K. Terrain aided underwater navigation using pockmarks OCEANS pp. 1-6 2009.

  • 3. Ayanna M.H. Fuzzy-logic based selection of surface feature observations for small body proximity operations WAC ‘06. World Automation Congress pp. 1-6 2006.

  • 4. Bagnell J. A. Bradley D. and Silver D. et al. Learning for autonomous navigation Robotics & Automation Magazine Vol. 17 no. 2 pp. 74-84 2010.

  • 5. Carreno S. Wilson P. Ridao P. et al. A survey on terrain based navigation for AUVs OCEANSVol. 10 pp. 1-7 2010.

  • 6. Deborah K. Meduna Stephen M. Rock Robert S. McEwen. Closed-loop terrain relative navigation for AUVs with non-inertial grade navigation sensors 2010 IEEE/OES Autonomous Underwater Vehicles (AUV)pp. 1-8. 2010.

  • 7. Feng Q.T. A new method of terrain matching and its environmental adaptability reseach University of Defense Technology 2004.

  • 8. Kjetil Bergh Anonsen Ove Kent Hagen. Recent developments in the HUGIN AUV terrain navigation system OCEANS Vol. 9 pp. 1-7 2011.

  • 9. Nordlund P.J. and Gustafsson F. Marginalized particle filter for accurate and reliable terrain-aided navigation Aerospace and Electronic Systems Vol. 45 no. 4 pp. 1385-1399 2009.

  • 10. Marvin W. Roe M.E. and Trenchard M.C.L. Integrating vector overlay information into naval digital map systems. IEEE/AIAA 30th Digital Avionics Systems Conference pp. 6B5-1 - 6B5-9 2011.

  • 11. McPhail S. Stevenson P. Pebody M. et al. Challenges of using an AUV to find and map hydrothermal vent sites in deep and rugged terrains 2010 IEEE International Conference on Autonomous Underwater Vehicles (AUV) pp. 1-8 2010.

  • 12. Panahandeh Ghazaleh Jansson Magnus. Visionaided inertial navigation using planar terrain features. International Conference on Robot Vision and Signal Processing pp. 287-291 2011.

  • 13. Pullen J.D. and Allen J.S. Modeling studies of the coastal circulation off Northern California: shelf response to a major Eel river flood event Continental Shelf Research Vol. 20 pp. 2213-2238 2000.

  • 14. Reynaud S. and Louis C. A universal navigability map building approach for improving Terrain-Aided-Navigation accuracy. 2010 IEEE/ION Conference on Position Location and Navigation Symposium (PLANS) pp. 888-896 2010.

  • 15. Stalder S. Bleuler H. and Ura T. Terrain-based navigation for underwater vehicles using side scan sonar images. OCEANS pp. 1-3 2008.

Search
Journal information
Impact Factor

IMPACT FACTOR 2018: 1.214
5-year IMPACT FACTOR: 1.086

CiteScore 2018: 1.48

SCImago Journal Rank (SJR) 2018: 0.391
Source Normalized Impact per Paper (SNIP) 2018: 1.141

Cited By
Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 274 175 2
PDF Downloads 128 96 3