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

• Published in: International journal of control, automation, and systems Vol. 16; no. 6; pp. 2977 - 2989
• Main Authors: Kim, Taeyun, Kim, Jinwhan, Byun, Seung-Woo
• Format: Journal Article
• Language: English
• Published: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.12.2018; Springer Nature B.V; 제어·로봇·시스템학회
• Subjects: Algorithms; Computer simulation; Control; Engineering; Fault detection; Linear matrix inequalities; Mathematical analysis; Mathematical functions; Matrix methods; Mechatronics; Nonlinear systems; Regular Papers; Robotics; State estimation; 제어계측공학; nonlinear estimation; particle filter; terrainreferenced navigation; Rao-Blackwellized particle filter; Extended Kalman filter
• ISSN: 1598-6446; 2005-4092
• DOI: 10.1007/s12555-017-0504-5

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.