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DGPS Correction Prediction Using Artificial Neural Networks

Published online by Cambridge University Press:  20 April 2007

M. Mohasseb ,
A. El-Rabbany ,
O. Abd El-Alim  and
R. Rashad
M. Mohasseb
Affiliation:
(Ryerson University, Toronto) (Email: rabbany@ryerson.ca)
A. El-Rabbany
Affiliation:
(Ryerson University, Toronto) (Email: rabbany@ryerson.ca)
O. Abd El-Alim
Affiliation:
(Ryerson University, Toronto) (Email: rabbany@ryerson.ca)
R. Rashad
Affiliation:
(Ryerson University, Toronto) (Email: rabbany@ryerson.ca)
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Abstract

This paper focuses on modelling and predicting differential GPS corrections transmitted by marine radio-beacon systems using artificial neural networks. Various neural network structures with various training algorithms were examined, including Linear, Radial Biases, and Feedforward. Matlab Neural Network toolbox is used for this purpose. Data sets used in building the model are the transmitted pseudorange corrections and broadcast navigation message. Model design is passed through several stages, namely data collection, preprocessing, model building, and finally model validation. It is found that feedforward neural network with automated regularization is the most suitable for our data. In training the neural network, different approaches are used to take advantage of the pseudorange corrections history while taking into account the required time for prediction and storage limitations. Three data structures are considered in training the neural network, namely all round, compound, and average. Of the various data structures examined, it is found that the average data structure is the most suitable. It is shown that the developed model is capable of predicting the differential correction with an accuracy level comparable to that of beacon-transmitted real-time DGPS correction.

Keywords

Differential GPSPseudorange differential correctionArtificial Neural Networks
Type
Research Article
Information
The Journal of Navigation , Volume 60 , Issue 2 , May 2007 , pp. 291 - 301
Copyright
Copyright © The Royal Institute of Navigation 2007

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References

REFERENCES

Bose, N., and Liang, P. (1996). Neural Network Fundamentals with Graphs, Algorithms, and Application. McGraw-Hill, International Edition, USA.Google Scholar
Chiang, K.-W. (2004). INS/GPS Integration Using Neural Networks For Land Vehicle Navigation Application. PhD thesis. The University of Calgary, Canada.Google Scholar
El-Rabbany, A. (2006). Introduction to GPS: The Global Positioning System. Second edition. Artech House Publishers.Google Scholar
Fausett, L. (1994). Fundamentals of Neural Networks Architectures, Algorithms and Applications. Prentice Hall International, Inc., USA.Google Scholar
Hagan, M., and Denuth, M. (1995). Neural Network Design. Thomson Learning, USA.Google Scholar
Jwo, D., and Pai, C. (2004). Incorporation of Neural Network State Space Estimator for GPS Attitude Determination. The Journal of Navigation, 57, 117–134.CrossRefGoogle Scholar
Masters, T. (1993). Practical Neural Network Recipes in C++. Academic Press, Inc., USA.Google Scholar
Suna, B., and Kalenderli, O. (2003). Electrode Contour Optimization By Artificial Neural Network With Levenberg-Marquartdt Algorithm. IJCI Proceedings of Intl. XII. Turkish Symposium on Artificial Intelligence and Neural Networks, 1, 1.Google Scholar
Watts, D. C. (2001). Land Cover Mapping by Combinations of Multiple Artificial Neural Networks. MSc thesis. The University of Calgary, Canada.Google Scholar