KNOWLEDGE-BASED METHODS FOR OPTIMUM APPROXIMATION OF GEOMETRIC DILUTION OF PRECISION

Abstract

Global Positioning System (GPS) satellites signal processing to obtain all in view satellite measurements and to use them to find a solution and to do integrity monitoring forms a major component of the load on the receiver's processing element. If processing capability is limited there is restriction on the number of measurements which can be obtained and processed. Alternatively, the number of measurements can be restricted and the resulting saving in load on the processor can be used to offer more spare processing time which can be used for other user specific requirements. Thus if m visible satellites can provide measurements only n measurements can be used (n < m). The arrangement and the number of GPS satellites influence measurement accuracy. Dilution of Precision (DOP) is an index evaluating the arrangement of satellites. Geometric DOP (GDOP) is, in effect, the amplification factor of pseudo-range measurement errors into user errors due to the effect of satellite geometry. The GDOP approximation is an essential feature in determining the performance of a positioning system. In this paper, knowledge-based methods such as neural networks and evolutionary adaptive filters are presented for optimum approximation of GDOP. Without matrix inversion required, the knowledge-based approaches are capable of evaluating all subsets of satellites and hence reduce the computational burden. This would enable the use of a high-integrity navigation solution without the delay required for many matrix inversions. Models validity is verified with test data. The results are highly effective techniques for GDOP approximation.