北京航空航天大学学报 ›› 2021, Vol. 47 ›› Issue (1): 1-6.doi: 10.13700/j.bh.1001-5965.2019.0644

• 论文 • 上一篇    下一篇

改进粒子群优化的卫星导航选星算法

王尔申1,2, 孙彩苗1, 黄煜峰1, 李轩1, 别玉霞1, 曲萍萍1   

  1. 1. 沈阳航空航天大学 电子信息工程学院, 沈阳 110136;
    2. 沈阳航空航天大学 辽宁省通用航空重点实验室, 沈阳 110136
  • 收稿日期:2019-12-24 发布日期:2021-01-29
  • 通讯作者: 王尔申 E-mail:wanges_2016@126.com
  • 作者简介:王尔申,男,博士,教授。主要研究方向:卫星导航、航空电子技术。
  • 基金资助:
    国家自然科学基金(61571309,61901284);辽宁省重点研发计划(2020JH2/10100045);辽宁省“百千万人才工程”(04021407);辽宁省自然科学基金(2019-MS-251);辽宁省教育厅科研项目(L201705,L201716);辽宁省高等学校优秀人才支持计划(LR2016069);辽宁省“兴辽英才计划”(XLYC1907022);沈阳市高层次创新人才计划(RC190030)

Satellite navigation satellite selection algorithm based on improved particle swarm optimization

WANG Ershen1,2, SUN Caimiao1, HUANG Yufeng1, LI Xuan1, BIE Yuxia1, QU Pingping1   

  1. 1. College of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China;
    2. Liaoning General Aviation Key Laboratory, Shenyang Aerospace University, Shenyang 110136, China
  • Received:2019-12-24 Published:2021-01-29

摘要: 为提高选星算法的性能,提出一种基于人工鱼群算法的粒子群优化(PSO)选星算法。该算法利用人工鱼群算法良好的全局收敛特性,克服了粒子群优化算法易陷入局部最优的缺点。将每种卫星组合看作空间中的一个粒子,选取几何精度因子(GDOP)作为适应度函数。利用所提算法更新粒子自身位置,优化卫星组合与几何精度因子。利用实际数据对所提算法进行验证和对比,结果表明:改进的选星算法在保障选星效率的同时,选星结果的准确性优于标准的粒子群优化选星算法。

关键词: 卫星导航, 选星, 几何精度因子(GDOP), 粒子群优化(PSO)算法, 人工鱼群算法

Abstract: 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.

Key words: satellite navigation, satellite selection, Geometric Dilution of Precision (GDOP), Particle Swarm Optimization (PSO) algorithm, artificial fish swarm algorithm

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