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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: [email protected])
A. El-Rabbany
Affiliation:
(Ryerson University, Toronto) (Email: [email protected])
O. Abd El-Alim
Affiliation:
(Ryerson University, Toronto) (Email: [email protected])
R. Rashad
Affiliation:
(Ryerson University, Toronto) (Email: [email protected])
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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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