PSEUDOLITE LAYOUT STRATEGY FOR BEIDOU SATELLITE NAVIGATION SYSTEM BASED ON MODIFIED DIFFERENTIAL EVOLUTION ALGORITHM

被引:0
|
作者
Chen, Zhipeng [1 ]
Zhao, Long [2 ]
Liu, Xiao [1 ]
Tao, Ye [1 ]
机构
[1] Beihang Univ, Sch Automat Sci & Elect Engn, 37 Xueyuan Rd, Beijing 100083, Peoples R China
[2] Beihang Univ, Digital Nav Ctr, 37 Xueyuan Rd, Beijing 100083, Peoples R China
基金
中国国家自然科学基金; 北京市自然科学基金; 国家重点研发计划;
关键词
D O I
暂无
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
According to the construction and development plan of Beidou satellite navigation system, Beidou III satellite navigation system is planned to be completed around 2020 and to provide services to the world. To improve reliability, availability and service accuracy of the system, and enable the system to provide users with high precision navigation, positioning and timing services in "Urban Canyon" and other special circumstances, it can be considered to establish space based pseudolite augmentation system. Like the traditional satellite navigation system, the positioning accuracy of the pseudolite augmentation system is closely related to the geometric distribution of the pseudolites. Because of its strong global optimization capability, the differential evolution algorithm (DE) is used in this paper for completing the optimization of pseudolite layout strategy. The GDOP (Geometric Dilution of Precision) value of the pseudolite system is set to the value of the objective function. At the same time, aiming at the problem that if the basic differential evolution algorithm deals with the high-dimensional function, the convergence rate will be slowed down, the "social cognition part" of the particle swarm optimization algorithm (PSO) is introduced into the basic differential evolution algorithm, and then, a modified differential evolution algorithm is proposed. This algorithm firstly adds stochastic mutation operation to the basic differential evolution algorithm, to increase the diversity of the population and improve the search ability of the algorithm. At the same time, the mutation factor is dynamically adjusted so that the algorithm avoids premature convergence and has strong local search capabilities in the later period. In addition, the optimal individuals in the current population are added in the crossover operation step, and the convergence rate of the algorithm is improved. Simulation results show that compared with the basic differential evolution algorithm, the modified differential evolution algorithm has better optimization performance and convergence rate, which is helpful to find an optimal space-based pseudolite augmentation system networking layout strategy and enhance the positioning accuracy of the pseudolite augmentation system.
引用
收藏
页码:739 / 752
页数:14
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