Abstract
The Global Positioning System, also known with the acronym GPS, is today widely used in civil and military applications, as for correct object positioning, as in other fields (ionospheric inferences, soil mapping and characterization, and so on). A limitation in the accuracy retrievable by differential GPS measures is due to multipath error which arises when GPS signal is reflected by surfaces around the antenna. In particular, many GPS receivers are projected with firmware implemented by means of classical mathematic algorithms, which can minimize multipath errors. This paper describes project criteria and experimental results obtained by a multipath rejection system based on Radial Basis Function Neural Networks, compared with measures retrieved by a commercial Differential GPS receiver.
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