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 (GOOP), which is given by the trace of the inverse of the measurement matrix. Ari 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 computationaJ benefit of neural network-based satellite selection is discussed.

No abstract

Ablltract. A method of combining Kalman filtering and minimax filtering is proposed and demonstrated in an application to phase-locked loop design. Kalman filtering suffers from a lack of robustness to departures from the aaaumed noise statistics. Minimax filtering, however, has the drawback of ignoring the engineer's {admittedly incomplete) knowledge of the noise statistics. It is shown in this paper that hybrid Kalman/minimax filtering can provide the "best of both worlds'~ Phase-locked loop filter design is used in this paper to demonstrate an application of hybrid estimation.

No abstract

No abstract