Document Type
Article
Publication Date
5-1995
Publication Title
Neurocomputing
Abstract
The application of neural networks to optimal satellite subset selection for navigation use is discussed. The methods presented in this paper are general enough to be applicable regardless of how many satellite signals are being processed by the receiver. The optimal satellite subset is chosen by minimizing a quantity known as Geometric Dilution of Precision (GDOP), which is given by the trace of the inverse of the measurement matrix. An artificial neural network learns the functional relationships between the entries of a measurement matrix and the eigenvalues of its inverse, and thus generates GDOP without inverting a matrix. Simulation results are given, and the computational benefit of neural network-based satellite selection is discussed.
Repository Citation
Simon, Daniel J. and El-Sherief, Hossny, "Navigation Satellite Selection Using Neural Networks" (1995). Electrical and Computer Engineering Faculty Publications. 133.
https://engagedscholarship.csuohio.edu/enece_facpub/133
Original Citation
Dan Simon, Hossny El-Sherief. (1995) Navigation satellite selection using neural networks. Neurocomputing, 7(3), 247-258, doi: 10.1016/0925-2312(94)00024-M.
DOI
10.1016/0925-2312(94)00024-M
Version
Postprint
Publisher's Statement
NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, 7, 3, (05-01-1995); 10.1016/0925-2312(94)00024-M
Volume
7
Issue
3
Included in
Digital Communications and Networking Commons, Electrical and Computer Engineering Commons
