AbstractGlobal navigation satellite systems (GNSS), such as the Global Positioning System (GPS) have been increasingly used in navigation and tracking of vehicles. Using GPS, certain positioning errors and limitations, such as multipath effects and the geometric position of the satellites (DOP) or signal obstructions by high buildings, trees and terrain, have to be considered. Generally travel on road or footpath, map-matching algorithms can be used to correlate the computed system location with a digital map network. Map Matched GPS (MMGPS) is a test-bed simulator for researching algorithms and techniques to reduce the error in position provided by a low cost stand-alone GPS receiver. In order to correctly map-match the GPS positions, a decision about the correct road can be difficult, especially at road junctions, slip roads or almost parallel roads.
Investigations into the use of artificial neural networks (ANNs) for reliability and accuracy improvement of map-matched GPS positioning was initiated in previous research [Winter, 2002]. However, there are generally strong interference effects that lead to slow learning and poor generalization when a single ANN is trained to perform different subtasks on different occasions [Jacobs et al., 1991], e.g. correct transport network (TN) segment selection considering different TN geometry.
Interference can be reduced by training a system composed of several different "expert" ANNs using a TN geometry indicator to decide which of the experts should be used for each training case. An aim of this research was the design, development and implementation of such a modular neural network (MNN). This work uses a new measure for indicating TN geometry, directly derived from GPS positions in MMGPS. An improvement of more than 50% to traditional map-matching techniques was achieved using the proposed MNN approach, when the correct road could not be uniquely identified by map-matching.
|Date of Award
- Global positioning systems
- Neural networks