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Underwater terrain navigation during realistic scenarios

Lager, Mårten LU ; Topp, Elin A. LU and Malec, Jacek LU (2018) 13th IEEE International Conference on Multisensor Integration and Fusion, IEEE MFI 2017 In Lecture Notes in Electrical Engineering 501. p.186-209
Abstract

Many ships today rely on Global Navigation Satellite Systems (GNSS), for their navigation, where GPS (Global Positioning System) is the most well-known. Unfortunately, the GNSS systems make the ships dependent on external systems, which can be malfunctioning, be jammed or be spoofed. There is today some proposed techniques where, e.g., bottom depth measurements are compared with known maps using Bayesian calculations, which results in a position estimation. Both maps and navigational sensor equipment are used in these techniques, most often relying on high-resolution maps, with the accuracy of the navigational sensors being less important. Instead of relying on high-resolution maps and low accuracy navigation sensors, this paper... (More)

Many ships today rely on Global Navigation Satellite Systems (GNSS), for their navigation, where GPS (Global Positioning System) is the most well-known. Unfortunately, the GNSS systems make the ships dependent on external systems, which can be malfunctioning, be jammed or be spoofed. There is today some proposed techniques where, e.g., bottom depth measurements are compared with known maps using Bayesian calculations, which results in a position estimation. Both maps and navigational sensor equipment are used in these techniques, most often relying on high-resolution maps, with the accuracy of the navigational sensors being less important. Instead of relying on high-resolution maps and low accuracy navigation sensors, this paper presents an implementation of the opposite, namely using low-resolution maps, but compensating this by using high-accuracy navigational sensors and fusing data from both bottom depth measurements and magnetic field measurements. A Particle Filter uses the data to estimate a position, and as a second step, a Kalman Filter enhances the accuracy even further. The algorithm has been tuned and evaluated using both a medium and a high-accuracy Inertial System. Comparisons of the various tuning methods are presented along with their performance results. The results from the simulated tests, described in this paper, show that for the high-end Inertial System, the mean position error is 10.2 m, and the maximum position error is 33.0 m during a 20 h test, which in most cases would be accurate enough to use for navigation.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Kalman filter, Particle filter, Safe navigation, Sensor fusion
host publication
Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System - An Edition of the Selected Papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems MFI 2017
series title
Lecture Notes in Electrical Engineering
volume
501
pages
24 pages
publisher
Springer
conference name
13th IEEE International Conference on Multisensor Integration and Fusion, IEEE MFI 2017
conference location
Daegu, Korea, Republic of
conference dates
2017-11-16 - 2017-11-22
external identifiers
  • scopus:85049944223
ISSN
1876-1100
1876-1119
ISBN
9783319905082
DOI
10.1007/978-3-319-90509-9_11https://doi.org/10.1007/978-3-319-90509-9_11
project
Digital Cognitive Companion for Marine Vessels
Wallenberg AI, Autonomous Systems and Software Program at Lund University
language
English
LU publication?
yes
id
d25468a8-5ecc-4037-871c-da30ef1e04a7
date added to LUP
2018-08-02 13:47:06
date last changed
2020-01-22 07:10:41
@inproceedings{d25468a8-5ecc-4037-871c-da30ef1e04a7,
  abstract     = {<p>Many ships today rely on Global Navigation Satellite Systems (GNSS), for their navigation, where GPS (Global Positioning System) is the most well-known. Unfortunately, the GNSS systems make the ships dependent on external systems, which can be malfunctioning, be jammed or be spoofed. There is today some proposed techniques where, e.g., bottom depth measurements are compared with known maps using Bayesian calculations, which results in a position estimation. Both maps and navigational sensor equipment are used in these techniques, most often relying on high-resolution maps, with the accuracy of the navigational sensors being less important. Instead of relying on high-resolution maps and low accuracy navigation sensors, this paper presents an implementation of the opposite, namely using low-resolution maps, but compensating this by using high-accuracy navigational sensors and fusing data from both bottom depth measurements and magnetic field measurements. A Particle Filter uses the data to estimate a position, and as a second step, a Kalman Filter enhances the accuracy even further. The algorithm has been tuned and evaluated using both a medium and a high-accuracy Inertial System. Comparisons of the various tuning methods are presented along with their performance results. The results from the simulated tests, described in this paper, show that for the high-end Inertial System, the mean position error is 10.2 m, and the maximum position error is 33.0 m during a 20 h test, which in most cases would be accurate enough to use for navigation.</p>},
  author       = {Lager, Mårten and Topp, Elin A. and Malec, Jacek},
  booktitle    = {Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System - An Edition of the Selected Papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems MFI 2017},
  isbn         = {9783319905082},
  issn         = {1876-1100},
  language     = {eng},
  month        = {01},
  pages        = {186--209},
  publisher    = {Springer},
  series       = {Lecture Notes in Electrical Engineering},
  title        = {Underwater terrain navigation during realistic scenarios},
  url          = {http://dx.doi.org/10.1007/978-3-319-90509-9_11https://doi.org/10.1007/978-3-319-90509-9_11},
  doi          = {10.1007/978-3-319-90509-9_11https://doi.org/10.1007/978-3-319-90509-9_11},
  volume       = {501},
  year         = {2018},
}