Abstract
A method for real-time positioning of LEO satellites using dual frequency GPS receivers is pre- sented. It is based on an a priori ground estimation of a pseudorange multipath map computed by means of a Self-Organizing Map neural network algorithm. The generated map characterizes the multipath environment of the satellite. This a priori estimation allows a real time correction of the pseudorange observables onboard the LEO satellite with a number of parameters affordable for space applications in terms of CPU and memory usage. The novelty of the approach consists of the use of neural networks to reduce the number of parameters and the use of a hybrid offline-online method. Precise IGS clocks and orbits have been used to measure the impact of these corrections in the navigation solution. Improvements in 3D positioning error of about 40%-50% for SAC-C (obtaining errors � 90cm) and 25%-35% for CHAMP (obtaining errors � 70cm) are demonstrated.
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