The Navigation Satellite Selection using a Subspace Adaptive Genetic Algorithm for High Earth Orbits in Multi-GNSS Scenarios

Xiaokun Zhang, Hong Yuan, Yingkui Gong, Lijuan Xu

Abstract:	The purpose of this work is to design a new GNSS satellite selection method using a subspace adaptive genetic algorithm (called GS-SAGA in this paper) for High Earth Orbit (HEO) multi-constellation receivers. A HEO is an orbit whose apogee does not lie below that of a geosynchronous orbit and that of all the GNSS satellites whether in services on orbits or under construction in progress around the world. So, for a HEO GNSS receiver, the antenna is oriented in the nadir direction, different from a LEO GNSS receiver in zenith direction and the weak signals may arrive non-deterministically from the opposite side of the Earth. GS-SAGA can select satellites, giving certain priorities to satellites with relatively high power levels signals and achieving the suboptimal geometric dilution of precision (GDOP). Firstly, the GNSS satellite visibility model for a HEO receiver is proposed, considering the HEO aperture angle, the HEO nadir receiving antenna pattern, the Earth tangent horizon mask, the GNSS satellite nadir transmitting antenna pattern and the signal power threshold of the HEO receiver. The visibility model defines whether a GNSS satellite is visible or not. Then, a subspace adaptive genetic algorithm is adopted to optimize the satellite selection. To avoid searching the space explosively and reduce the use of onboard computer resource and time, GS-SAGA constructs a smaller searching space through setting the signal power level threshold, the GDOP threshold, the maximum number of selected satellites and giving a certain priority to GNSS satellites whose transmitting signals are received by the HEO receiver with relatively high power levels. And If the GDOP value is below the GDOP threshold, the GSSAGA process will terminate, achieving the suboptimal GDOP within the allowable time. Simulation results shows GS-SAGA have the capabilities to estimate the satellite geometries and the signal power levels in real time, offering excellent signal power levels and GDOP values of the selection satellites. Meanwhile, GS-SAGA can decrease the number of signals used for onboard orbit determination and reduce the number of necessary capturing and tracking channels.
Published in:	Proceedings of the 2013 International Technical Meeting of The Institute of Navigation January 29 - 27, 2013 Catamaran Resort Hotel San Diego, California
Pages:	172 - 179
Cite this article:	Zhang, Xiaokun, Yuan, Hong, Gong, Yingkui, Xu, Lijuan, "The Navigation Satellite Selection using a Subspace Adaptive Genetic Algorithm for High Earth Orbits in Multi-GNSS Scenarios," Proceedings of the 2013 International Technical Meeting of The Institute of Navigation, San Diego, California, January 2013, pp. 172-179.
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