A Novel Satellite Selection Method Based On Genetic Algorithm

With the modernization of Global Navigation Satellite System (GNSS), satellites in view dramatically grow to 35. Tracking all visible satellites simultaneously costs huge computation sources. In this paper, we propose a novel satellite selection method based on genetic algorithm (SSMGA), which could select exact satellites from multi-constellation with optimized Geometric Dilution of Precision (GDOP) values. After several experiments, the key parameters of genetic algorithm have been determined by selection, crossover and mutation. In the meantime, the local optimal solutions of satellite selection is solved. SSMGA has been evaluated under multi-constellation and variable navigation satellites combination. The simulation results between SSMGA and traditional optimal satellite selection algorithm (TOSSA) indicate that SSMGA has a significant improvement on the computational complexity with the same accuracy according to GDOP. The time consumption of SSMGA dramatically decreases 70% compared with TOSSA in three satellite selection configurations among four constellation: GPS, BDS, Galileo and GLONASS.