NAVIGATION SATELLITE SELECTION USING NEURAL NETWORKS

The application of neural networks to optimal satellite subset selecti on for navigation use is discussed. The methods presented in this pape r are general enough to be applicable regardless of how many satellite signals are being processed by the receiver. The optimal satellite su bset is chosen by minimizing a quantity known as Geometric Dilution of Precision (GDOP), which is given by the trace of the inverse of the m easurement matrix. An artificial neural network learns the functional relationships between the entries of a measurement matrix and the eige nvalues of its inverse, and thus generates GDOP without inverting a ma trix. Simulation results are given, and the computational benefit of n eural network-based satellite selection is discussed.