北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2021, Vol. 44 ›› Issue (3): 9-14.doi: 10.13190/j.jbupt.2020-168

• 论文 • 上一篇    下一篇

基于几何分布和差分进化的双模导航选星算法

朱军1, 许士杰1, 李凯2   

  1. 1. 安徽大学 电子信息工程学院, 合肥 230601;
    2. 上海科技大学 创意与艺术学院, 上海 201210
  • 收稿日期:2020-09-02 出版日期:2021-06-28 发布日期:2021-06-23
  • 作者简介:朱军(1968-),女,副教授,硕士生导师,E-mail:junzhu@ahu.edu.cn.
  • 基金资助:
    安徽省科技重大专项项目(18030901010)

Dual-Mode Navigation Satellite Selection Algorithm Based on Differential Evolution and Geometry

ZHU Jun1, XU Shi-jie1, LI Kai2   

  1. 1. School of Electronics and Information Engineering, Anhui University, Hefei 230601, China;
    2. School of Creativity and Art, ShanghaiTech University, Shanghai 201210, China
  • Received:2020-09-02 Online:2021-06-28 Published:2021-06-23

摘要: 基于双系统集成场景,对几何精度因子建模.针对传统算法实时性低的问题,提出了一种基于卫星几何分布和差分进化的选星算法.根据仰角分布和系统种类,确定卫星组合几何分布;通过设置不同的适应度阈值和根据剩余卫星数目,自适应改变种群大小,实现了快速选星.仿真结果表明,所提算法与传统算法相比,差值范围为0~0.25,单时刻百次选星平均耗时为传统算法的8.09%,该算法可应用于可见卫星数目增加的双模导航场景中.

关键词: 双模导航, 卫星选择, 几何分布, 差分进化, 自适应种群

Abstract: Based on the dual-system integration scenario, the geometric dilution of precision is modeled. Due to the low real-time performance of the traditional algorithm, a satellite selection algorithm based on the geometric distribution of satellites and differential evolution is proposed. According to the distribution of the elevation angle and the system type, the distribution of the satellite combination is determined. By setting different fitness thresholds and adaptively changing and the population size according to the number of remaining satellites, the rapid satellite selection is realized. Simulations show that, compared with the traditional algorithms, the proposed algorithm has a difference range from 0 to 0.25. And the average time to select a hundred times at a single time is 8.09% of the traditional algorithm; Moreover, this algorithm can be applied to dual-mode navigation scenarios where the number of visible satellites increases.

Key words: dual-mode navigation, satellite selection, geometric distribution, differential evolution, self-adaptive population

中图分类号: