| Citation: | WANG Ershen, YANG Di, WANG Chuanyun, et al. Parameter analysis and improvement of PSO satellite selection algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(11): 2133-2138. doi: 10.13700/j.bh.1001-5965.2019.0138(in Chinese)
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Multi-constellation integrated navigation can provide users with more visible satellites; however, the computational complexity of the navigation receiver will also be increased. Therefore, part visible satellites are selected instead of all visible satellites for receiver position solution, which becomes a hot spot in satellite selection algorithm research. The particle swarm optimization (PSO) is introduced into the satellite selection process by the PSO fast satellite selection algorithm. Through this method, not only the time for selecting satellite is reduced, but also the fast selection of the Beidou/GPS integrated constellation is implemented. The influence of the algorithm's key parameters such as inertia weighting factor, acceleration coefficient and population size on the performance of PSO satellite selection algorithm is studied. In addition, since PSO satellite selection algorithm is easy to fall into the local optimum for the search process, the adaptive simulated annealing particle swarm optimization (ASAPSO) algorithm is proposed to optimize the process of satellite selection algorithm. Moreover, the adaptive adjustment of evolutionary parameters with adaptive value, the adjustment of particle velocity in combination with simulated annealing algorithm are introduced in order to enhance the ability of the algorithm to jump out of local extremum. The algorithm is verified by using real navigation data, and the results demonstrate that the ASAPSO algorithm not only can ensure the satellite selection time, but also can improve the accuracy of the search results. Moreover, the performance of the ASAPSO satellite selection algorithm is better than that of the PSO satellite selection algorithm.
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