DocumentCode
1155496
Title
Terrain navigation for underwater vehicles using the correlator method
Author
Nygren, Ingemar ; Jansson, Magnus
Author_Institution
Dept. of Signals, R. Inst. of Technol., Stockholm, Sweden
Volume
29
Issue
3
fYear
2004
fDate
7/1/2004 12:00:00 AM
Firstpage
906
Lastpage
915
Abstract
In many autonomous underwater vehicle (AUV) applications of long duration, there is a need to accurately determine the AUV´s position in an effective way, in terms of both cost and energy consumption. Such situations may occur in reconnaissance operations, mine counter measure operations, and when an AUV is sampling the characteristics of the environment along a track. Since underwater maps are rare, there also is a need for navigational methods that can accommodate very infrequent sampling in terms of time and space. This work describes a new method to solve the above problem by using a multibeam sonar for three-dimensional sampling of the bottom topography and a linear Kalman filter in a nearly optimal way. The proposed method is very accurate, robust, and computationally efficient compared to other methods that are used for terrain navigation. This method is also suitable for continuous navigation.
Keywords
Kalman filters; correlation methods; maximum likelihood estimation; navigation; recursive filters; sonar signal processing; underwater vehicles; 3D sampling; Bayesian recursive filter; autonomous underwater vehicle; bottom topography; correlator method; energy consumption; linear Kalman filter; maximum-likelihood estimation; multibeam sonar; terrain navigation; Correlators; Costs; Counting circuits; Energy consumption; Reconnaissance; Sampling methods; Sonar navigation; Surfaces; Underwater tracking; Underwater vehicles; 65; Bayesian recursive filter; ML; correlation; estimation; linear Kalman filter; maximum-likelihood; terrain navigation;
fLanguage
English
Journal_Title
Oceanic Engineering, IEEE Journal of
Publisher
ieee
ISSN
0364-9059
Type
jour
DOI
10.1109/JOE.2004.833222
Filename
1353440
Link To Document