Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-27T03:45:59.011Z Has data issue: false hasContentIssue false
  • English
  • Français

Integration of Navigation Data

Published online by Cambridge University Press:  21 October 2009

A. Svensson  and
J. Holst
A. Svensson
Affiliation:
(Lund Institute of Technology)
J. Holst
Affiliation:
(Lund Institute of Technology)
Get access Rights & Permissions [Opens in a new window]

Abstract

This article treats integration of navigation data from a variety of sensors in a submarine using extended Kalman filtering in order to improve the accuracy of position, velocity and heading estimates. The problem has been restricted to planar motion. The measurement system consists of an inertial navigation system, a gyro compass, a passive log, an active log and a satellite navigation system. These subsystems are briefly described and models for the measurement errors are given.

Four different extended Kalman filters have been tested by computer simulations. The simulations distinctly show that the passive subsystems alone are insufficient to improve the estimate of the position obtained from the inertial navigation system. A log measuring the velocity relative to the ground or a position determining system are needed. The improvement depends on the accuracy of the measuring instruments, the extent of time the instrument can be used and which filter is being used. The most complex filter, which contains fourteen states, eight to describe the motion of the submarine and six to describe the measurement system, including a model of the inertial navigation system, works very well.

Keywords

1. Integrated systems.2. Errors and accuracy.3. Inertial navigation.
Type
Research Article
Information
The Journal of Navigation , Volume 48 , Issue 1 , January 1995 , pp. 114 - 135
Copyright
Copyright © The Royal Institute of Navigation 1995

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1Beck, N. and Haals, P. (1985). Prediction of the Position of a Ship (in Danish: Positions prediktor till skibe). Masters Thesis no 13/85, Institute of Mathematical Statistics and Operations Research and Institute of Automatic Control Systems, Technical University of Denmark, Lyngby, Denmark.Google Scholar
2Blanke, M. (1981). Ship Propulsion Losses Related to Automatic Steering and Prime Mover Control. PhD Thesis, Institute of Automatic Control Systems, Technical University of Denmark, Lyngby, Denmark.Google Scholar
3Chui, C. K. and Chen, G. (1991). Kalman Filtering. Springer-Verlag, 2nd edition.CrossRefGoogle Scholar
4Gelb, A.Kasper, J. F. Jr.Nash, R. A. Jr.Price, C. F. and Sutherland, A. A. (1974). Applied Optimal Estimation. The MIT Press, Cambridge, Massachusetts.Google Scholar
5Grewal, M. S.Henderson, V. D. and Miyasako, R. S. (1991). Application of Kalman Filtering to the Calibration and Alignment of Inertial Navigation Systems. IEEE Transactions on Automatic Control, vol. AC-36, pp. 4–13.CrossRefGoogle Scholar
6Svensson, A. (1992). Cofiltering of Navigation Data (in Swedish: Samfihrering av navigationsdata). Masters Thesis 1992:E7, Department of Mathematical Statistics, Lund Institute of Technology, Lund, Sweden.Google Scholar
7Åström, K. J. and Källström, C. G. (1976). Identification of Ship Steering Dynamics. Automatica, vol. 12, pp. 9–22.CrossRefGoogle Scholar
8 IT4-report, Appendix (1990). Basic Principlesfor Inertial Navigation Systems and Gyro Compasses (in Swedish: Grundprincipenper för tröghetsplattformar och gyrokompasser). Wolffdata, Lidingö, Sweden.Google Scholar
9Notes for a Course in Gyro- and INS-technique, part 1, Basics (in Swedish: kompendium i gyro- och TN-teknik del 1, grunder). Wolffdata, Lidingö, Sweden.Google Scholar