全球定位系统2025，Vol.50Issue(2)：126-134,9.DOI:10.12265/j.gnss.2025005
基于多策略融合的灰狼优化算法的GNSS快速选星方法
A fast satellite selection method for GNSS systems based on multi-strategy grey wolf optimization algorithm
摘要
Abstract
The rapid advancement of GNSS now enables GNSS terminals to simultaneously observe dozens of satellites.Nevertheless,implementing positioning solutions for all observed satellites would substantially escalate the demand for computing resources and performance capabilities of small mobile terminals.To address this issue,this paper introduces an efficient satellite selection algorithm utilizing the multi-strategy grey wolf optimization(MSGWO)algorithm.The MSGWO method integrates the grey wolf optimizer(GWO)algorithm while incorporating the crossover strategy from variable neighborhood search(VNS)and the mutation mechanism from genetic algorithm(GA).Therefore,it can significantly mitigate the issues of premature convergence to local optima and inadequate population diversity in the GWO algorithm.The MSGWO algorithm is validated and comparatively analyzed using empirical data.The research findings indicate that,within a multi-system environment,the computational efficiency of the MSGWO algorithm improves by 92.38%compared to the exhaustive method.This algorithm is especially well-suited for addressing the complex problem of satellite selection in multi-constellation systems.关键词
快速选星/灰狼优化(GWO)/GNSS/几何精度因子(GDOP)/计算效率Key words
satellite selection/grey wolf optimizer(GWO)/GNSS/geometric dilution of precision(GDOP)/computational efficiency分类
天文与地球科学引用本文复制引用
吕葳,陈明剑,施星宇,沈洋,李玉星,佟帅..基于多策略融合的灰狼优化算法的GNSS快速选星方法[J].全球定位系统,2025,50(2):126-134,9.基金项目
智慧地球重点实验室自主科研基金(SYS-ZX02-2024-01) （SYS-ZX02-2024-01）
