JGIS  Vol.3 No.4 , October 2011
Performance Improvement of GPS GDOP Approximation Using Recurrent Wavelet Neural Network
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.

Cite this paper
nullS. Tafazoli and M. Mosavi, "Performance Improvement of GPS GDOP Approximation Using Recurrent Wavelet Neural Network," Journal of Geographic Information System, Vol. 3 No. 4, 2011, pp. 318-322. doi: 10.4236/jgis.2011.34029.
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