| Citation: | WANG Ershen, SUN Caimiao, HUANG Yufeng, et al. Satellite navigation satellite selection algorithm based on improved particle swarm optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(1): 1-6. doi: 10.13700/j.bh.1001-5965.2019.0644(in Chinese)
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In order to improve the performance of satellite selection algorithm, the Particle Swarm Optimization (PSO) satellite selection algorithm based on artificial fish swarm algorithm is proposed. Using the global convergence characteristics of artificial fish swarm algorithm, the algorithm can overcome the shortcomings of PSO algorithm that is easy to fall into local optimum. The improved algorithm treats each satellite combination as a particle in space, and the Geometric Dilution of Precision (GDOP) is chosen as the fitness function. The particle updates its position based on the optimization principle of the particle swarm optimization algorithm and artificial fish swarm algorithm, and the optimal satellite combination and GDOP value are obtained. The algorithms are verified and compared with real data, and the results show that the improved satellite selection algorithm not only guarantees the efficiency of the satellite selection, but also the accuracy of the satellite selection result is better than that of the satellite selection algorithm based on the PSO.
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