| Citation: | WANG Ershen, JIA Chaoying, QU Pingping, et al. BDS/GPS integrated navigation satellite selection algorithm based on chaos particle swarm optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(2): 259-265. doi: 10.13700/j.bh.1001-5965.2018.0281(in Chinese)
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In the process of signal receiving, global navigation satellite system (GNSS) receiver will be affected by factors such as building blockages and signal interference and will not be able to obtain all the visible satellites; moreover, in order to reduce the processing burden of multi-constellation receivers, the fast satellite selection algorithm using partial visible satellites to achieve positioning solution is investigated, and the BDS/GPS integrated navigation satellite selection algorithm based on chaos particle swarm optimization (CPSO) is proposed. First, the visible satellites are continuously numbered and randomly divided into groups. Each group is regarded as a particle. Then, chaotic maps are used to select several groups from all grouping spaces to form initial population. The geometric dilution of precision (GDOP) is chosen as fitness function to evaluate the particle's quality. In addition, the particle's position is updated by the velocity-displacement model of the PSO algorithm, and it gradually approaches the global optimal solution of the satellite combination with better geometric distribution of the space satellite. Finally, using real navigation data, the algorithm is verified by simulation experiments. The results demonstrate that when the number of selected satellite is more than 5, the time that the proposed algorithm takes to select satellite once is 37.5% of the time that the traversing algorithm takes, and the GDOP error of the selected satellites is between 0 and 0.6. Moreover, the proposed algorithm can be applied to the case of different numbers of selected satellite in BDS/GPS integrated navigation.
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