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Article
Navigation Satellite Selection Using Neural Networks
Neurocomputing
  • Daniel J. Simon, Cleveland State University
  • Hossny El-Sherief, TRW System Integration Group
Document Type
Article
Publication Date
5-1-1995
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.
DOI
10.1016/0925-2312(94)00024-M
Version
Postprint
Citation Information
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.