Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/13300
Title: Neural network model for differential GPS
Authors: Low, Kwong Hwee.
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems
Issue Date: 1998
Abstract: The Global Positioning System (GPS) is a worldwide satellite-based system consisting of a constellation of 24 satellites and their ground stations. GPS uses the satellites as reference points to calculate positions accurate to a matter of meters. In fact, with advanced forms of GPS, measurements to better than a centimeter can be made. GPS receivers have been miniaturized to just a few integrated circuits and so are becoming very economical, which makes the technology accessible to virtually everyone. In fact, GPS is finding its way into cars, boats, planes, construction equipment, movie making gear, farm machinery, and even laptop computers.
URI: http://hdl.handle.net/10356/13300
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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