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

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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.
Publisher
INST CONTROL ROBOTICS & SYSTEMS
Issue Date
2018-12
Language
English
Article Type
Article
Citation

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, v.16, no.6, pp.2977 - 2989

ISSN
1598-6446
DOI
10.1007/s12555-017-0504-5
URI
http://hdl.handle.net/10203/248240
Appears in Collection
ME-Journal Papers(저널논문)
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