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.
(Less)
- author
- Lager, Mårten
LU
; Topp, Elin A.
LU
and Malec, Jacek
LU
- organization
- publishing date
- 2018-01-01
- 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
- 1876-1119
- 1876-1100
- ISBN
- 9783319905082
- DOI
- 10.1007/978-3-319-90509-9_11
- project
- Digital Cognitive Companion for Marine Vessels
- language
- English
- LU publication?
- yes
- id
- d25468a8-5ecc-4037-871c-da30ef1e04a7
- date added to LUP
- 2018-08-02 13:47:06
- date last changed
- 2024-01-29 19:14:53
@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 = {{1876-1119}},
keywords = {{Kalman filter; Particle filter; Safe navigation; Sensor fusion}},
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_11}},
doi = {{10.1007/978-3-319-90509-9_11}},
volume = {{501}},
year = {{2018}},
}