A modular neural network approach to improve map-matched GPS positioning

Authors:
Marylin Winter;George Taylor
Affiliations:
Faculty of Advanced Technology, University of Glamorgan, Pontypridd, Wales, UK;Faculty of Advanced Technology, University of Glamorgan, Pontypridd, Wales, UK
Venue:
W2GIS'06 Proceedings of the 6th international conference on Web and Wireless Geographical Information Systems
Year:
2006

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Abstract

This paper provides an overview of work undertaken over the past two years to develop Artificial Neural Network (ANN) techniques to improve the accuracy and reliability of road selection during map-matching (MM) computation. MM positions provided by low-cost GPS receivers have great potential when integrated with hand-held or in-vehicle Geographical Information System (GIS) applications, especially those used for tracking and navigation, on path and road networks. The applied modular neural network (MNN) approach is using a suitable road shape indicator to incorporate different road shapes for local ANN training. MNN test results indicate good potential for the method to provide a significant improvement in MM and positional accuracy over traditional methods. Further results and conclusions of this on-going research will be published in due course.