Real time GPS Positioning of LEO Satellites Mitigating Pseudorange Multipath through Neural Networks

Pere Ramos-Bosch, Manuel Hernandez-Pajares, J. Miguel Juan, and Jaume Sanz

Peer Reviewed

Abstract: A method for real-time positioning of LEO satellites using dual frequency GPS receivers is presented. 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.
Published in: NAVIGATION, Journal of the Institute of Navigation, Volume 54, Number 4
Pages: 309 - 315
Cite this article: Ramos-Bosch, Pere, Hernandez-Pajares, Manuel, Juan, J. Miguel, Sanz, Jaume, "Real time GPS Positioning of LEO Satellites Mitigating Pseudorange Multipath through Neural Networks", NAVIGATION, Journal of The Institute of Navigation, Vol. 54, No. 4, Winter 2007-2008, pp. 309-315.
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