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.
Navigation Satellite Selection Using Neural NetworksNeurocomputing
Publisher's StatementNOTICE: 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
Citation InformationDan Simon, Hossny El-Sherief. (1995) Navigation satellite selection using neural networks. Neurocomputing, 7(3), 247-258, doi: 10.1016/0925-2312(94)00024-M.