Abstract
One of the most important factors affecting the precision of the performance of a GPS receiver is the relative positioning of satellites to each other. Therefore, it is essential to choose appropriate accessible satellites utilized in the calculation of GPS positions. Optimal subsets of satellites are determined using the least value of their Geometric Dilution of Precision (GDOP). The most correct method of calculating GPS GDOP uses inverse matrix for all combinations and selecting the lowest ones. However, the inverse matrix method, especially when there are so many satellites, imposes a huge calculation load on the processor of the GPS navigator. In this paper, the rapid and precise calculation of GPS GDOP based on Recurrent Wavelet Neural Network (RWNN) has been introduced for selecting an optimal subset of satellites. The method of NNs provides a realistic calculation approach to determine GPS GDOP without any need to calculate inverse matrix.
Highlights
Global Positioning System (GPS) is a satellite based positioning system which was rapidly grown in the past two decades
The rapid and precise calculation of GPS Geometric Dilution of Precision (GDOP) based on Recurrent Wavelet Neural Network (RWNN) has been introduced for selecting an optimal subset of satellites
The rapid and precise calculation of GPS GDOP using RWNN has been studied for the selection of an appropriate subset of navigator satellites
Summary
Global Positioning System (GPS) is a satellite based positioning system which was rapidly grown in the past two decades. As the vehicles using the GPS moves, the initial selected satellites disappear in horizon and become invisible to the human eye. At this stage, other appropriate satellites shall be selected [3,4]. The best method for calculating the Geometric Dilution of Precision (GDOP) of GPS satellites is to use inverse matrix for all configurations and selecting the smallest one; but, the inversion of matrix imposes a huge calculation load on the processor of the navigator [5].
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