NDLI: Filtering Depth Measurements in Underwater Vehicles for Improved Seabed Imaging

Content Provider	IEEE Xplore Digital Library
Author	Willumsen, A.B. Hagen, O.K. Boge, P.N.
Copyright Year	2007
Description	Author affiliation: Kongsberg Maritime AS, Horten (Willumsen, A.B.)
Abstract	Underwater vehicles are commonly used for detailed seabed mapping. To derive a bathymetric map, the depth of the underwater vehicle must be known. The offshore survey industry has often strong requirements on both absolute and relative accuracy. Depth is usually deduced from a measurement of absolute pressure. Regardless of the quality of the pressure sensor, the pressure measurement is influenced by environmental factors, such as waves, tide, atmospheric pressure and sea water density profile. In the paper we analyze these factors with respect to their behavior and effect on the measured depth. An underwater vehicle equipped with an aided inertial navigation system (AINS) has through its accelerometers and Doppler velocity log redundant information on the movement of the underwater vehicle. Using this information, an AINS is well suited to filter out dynamic pressure sensor noise. Linear wave theory is used to model the effects of the waves on the pressure sensor, and it is suggested how the parameters of the pressure sensor noise model can be deduced from the mean wave amplitude and period. AINS performance is often substantially improved by post-processing. The paper will therefore examine the depths outputted from the pressure sensor, from the AINS real-time filter, and from post processing the AINS measurements.
Starting Page	1
Ending Page	6
File Size	2334367
Page Count	6
File Format	PDF
ISBN	9781424406340
DOI	10.1109/OCEANSE.2007.4302249
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-18
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Filtering Underwater vehicles Sea measurements Pressure measurement Atmospheric measurements Filters Environmental factors Tides Atmospheric waves Inertial navigation Waves Kalman Filtering Imaging Inertial Navigation Pressure Depth
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Navigation and tracking of underwater vehicles: Kalman based and neural architectures

Depth accuracy in seabed mapping with underwater vehicles

A self-adaptive unscented Kalman filtering for underwater gravity aided navigation

Comparison of forward Vs. feedback Kalman filter for aided inertial navigation system

Particle filtering and Cramer-Rao lower bound for underwater navigation

Augmented state Kalman filtering for AUV navigation

An aided navigation post processing filter for detailed seabed mapping UUVs

Nonlinear filtering for terrain-referenced underwater navigation with an acoustic altimeter

Simultaneous estimation of sea level and underwater vehicle location

Filtering Depth Measurements in Underwater Vehicles for Improved Seabed Imaging

Similar Documents

Navigation and tracking of underwater vehicles: Kalman based and neural architectures

Depth accuracy in seabed mapping with underwater vehicles

A self-adaptive unscented Kalman filtering for underwater gravity aided navigation

Comparison of forward Vs. feedback Kalman filter for aided inertial navigation system

Particle filtering and Cramer-Rao lower bound for underwater navigation

Augmented state Kalman filtering for AUV navigation

An aided navigation post processing filter for detailed seabed mapping UUVs

Nonlinear filtering for terrain-referenced underwater navigation with an acoustic altimeter

Simultaneous estimation of sea level and underwater vehicle location

Filtering Depth Measurements in Underwater Vehicles for Improved Seabed Imaging