电力信息与通信技术2025，Vol.23Issue(6)：1-7,7.DOI:10.16543/j.2095-641x.electric.power.ict.2025.06.01
面向复杂环境的北斗信号统计特征聚类与北斗/INS组合导航性能优化
BeiDou Signal Statistical Feature Clustering and BeiDou/INS Integrated Navigation Performance Optimization for Complex Environments
摘要
Abstract
In complex navigation environments,variations in BeiDou satellite signal quality can significantly degrade the overall performance of integrated BeiDou/Inertial Navigation System(INS)solutions.To address this challenge,this paper proposes an adaptive BeiDou/INS integrated-navigation method based on clustering analysis of the statistical characteristics of BeiDou signals,with the goal of improving accuracy and stability under demanding conditions.First,multi-dimensional statistical features of the carrier-to-noise ratio,including signal strength,variance,skewness,and kurtosis,are extracted from raw BeiDou observations to construct a high-dimensional feature space that underpins subsequent clustering analysis.Next,a clustering algorithm is applied to explore the intrinsic distribution patterns of BeiDou signals in various navigation environments.By grouping signals with similar feature profiles into several representative categories,the method accurately distinguishes signal-quality differences that arise in complex settings.Finally,the clustering results are used to adaptively tune the Kalman filter's gain parameters for each signal category,enabling dynamic optimisation in both loosely coupled and tightly coupled BeiDou/INS navigation modes.Field experiments conducted under real-world conditions show that the proposed approach effectively adapts to changing environments and markedly enhances the positioning accuracy and robustness of the integrated navigation system in complex scenarios.关键词
北斗卫星导航系统/信号特征/聚类算法/导航环境感知Key words
beidou satellite navigation system/signal characteristics/clustering algorithms/navigation environment perception分类
电子信息工程引用本文复制引用
张涵,韩飞,王潜心,高明,刘满林,王晋波,王奕菲,徐杨,吴旭..面向复杂环境的北斗信号统计特征聚类与北斗/INS组合导航性能优化[J].电力信息与通信技术,2025,23(6):1-7,7.基金项目
国家重点研发计划项目(2020YFA0713502). （2020YFA0713502）
