A New Satellite Navigation Receiver Data Processing Method Using Improved PSO Algorithm and GDOP

Ershen Wang, Caimiao Sun, Chuanyun Wang, Pingping Qu, Yufeng Huang ,Tao Pang, Song Xiang, Song Xu

Abstract:	In order to overcome the problem that the PSO algorithm is easy to fall into local optimum during the satellite selection, and the accuracy of the satellite selection result is reduced to a certain extent, the improved method of PSO fast satellite selection based on artificial fish swarm algorithm (AFSA) is proposed. This method overcomes the shortcomings of PSO by using the good global convergence characteristics of AFSA. In the improved algorithm, each satellite combination is regarded as a particle in space, and the geometric dilution of precision (GDOP) is chosen as the fitness function of the particle. Based on the optimization principle of the PSO and AFSA algorithms, the particle updates its position until the end of the iteration. Finally, the optimal satellite combination and the minimum GDOP value are obtained. In order to reduce the complexity of the algorithm and ensure the convergence speed, the proposed algorithm will omit the foraging behavior of the AFSA algorithm. The theoretical derivations are validated using GNSS observation data. The results show that the improved satellite selection algorithm not only guarantees the speed of the satellite selection, but also the accuracy of the satellite selection result is better than the PSO fast satellite selection.
Published in:	Proceedings of the 2020 International Technical Meeting of The Institute of Navigation January 21 - 24, 2020 Hyatt Regency Mission Bay San Diego, California
Pages:	726 - 735
Cite this article:	Wang, Ershen, Sun, Caimiao, Wang, Chuanyun, Qu, Pingping, Huang, Yufeng, Pang, Tao, Xiang, Song, Xu, Song, "A New Satellite Navigation Receiver Data Processing Method Using Improved PSO Algorithm and GDOP," Proceedings of the 2020 International Technical Meeting of The Institute of Navigation, San Diego, California, January 2020, pp. 726-735. https://doi.org/10.33012/2020.17173
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