A Comparison of Nonlinear Filter Algorithms for Terrain-referenced Underwater Navigation

  • Taeyun Kim
  • Jinwhan KimEmail author
  • Seung-Woo Byun
Regular Papers Robot and Applications


Terrain-referenced navigation (TRN) uses topographic data to correct drift errors due to dead-reckoning or inertial navigation. While it has long been applied to aerial vehicle applications, TRN can be more useful for navigation in underwater environments where global positioning system signals are not available. TRN requires a geometric description of undulating terrain surface as a mathematical function or a look-up table, which leads to a nonlinear estimation problem. In this study, three nonlinear filter algorithms for underwater TRN are considered: 1) extended Kalman filter, 2) particle filter, and 3) Rao-Blackwellized particle filter. The performance of these three filters is compared through navigation simulations with actual bathymetry data.


Extended Kalman filter nonlinear estimation particle filter Rao-Blackwellized particle filter terrainreferenced navigation 


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Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringKAISTDaejeonKorea
  2. 2.Hanwha Systems Co. Ltd.Naval R&D CenterGyeongsangbuk-DoKorea

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