基于随机森林的GNSS观测粗差拟准检定方法  

Quasi-accurate detection method for GNSS observationgross errors based on random forest

作　　者：高旺 刘裕荐 郭雅娟 陶贤露 潘树国[1] Gao Wang;Liu Yujian;Guo Yajuan;Tao Xianlu;Pan Shuguo(School of Instrument Science and Engineering,Southeast University,Nanjing 210096,China;Research Institute,State Grid Jiangsu Electric Power Co.,Ltd,Nanjing 210008,China)

机构地区：[1]东南大学仪器科学与工程学院,南京210096 [2]国网江苏省电力有限公司电力科学研究院,南京210008

出　　处：《仪器仪表学报》2025年第1期42-53,共12页Chinese Journal of Scientific Instrument

基　　金：国家电网有限公司科技项目(5700-202318596A-3-2-ZN)资助。

摘　　要：在复杂观测环境中,全球导航卫星系统(GNSS)可用卫星数量显著减少,且观测信号易受多维粗差影响,导致粗差识别难、定位性能显著劣化。针对此问题,提出一种基于随机森林自适应分类的拟准检定抗差滤波方法。首先,基于城市环境下的实测动态观测数据,构建了一个基于GNSS观测多维数据特征的随机森林分类器,实现将观测数据自适应划分为拟准观测值和非拟准观测值两大类;其次,基于该分类器发展了一种新的GNSS拟准检定抗差定位方法,通过分类过程的拟准得分确定拟准观测解算的随机模型,并采用标准化后的真误差代替常规最小二乘残差进行后续的中国科学院地质与地球物理研究所的第3种抗差方案(IGG3)抗差检验。以伪距差分定位为例,在两种具有不同量级观测误差的定位场景中对所提方法的有效性进行了评估,实验结果表明,与常规的IGG3和拟准检方法相比,该方法定位性能显著提升,不同测试场景下的定位精度提升16%~51%。所提出的随机森林分类模型有效提高了拟准观测值的识别准确性,因此能够有效提升复杂观测环境下的定位性能。In complex observation environments,the number of available GNSS satellites is significantly reduced,and the observed signals are susceptible to multidimensional outliers,resulting in challenges in outlier detection and a marked deterioration in positioning performance.To address this issue,this paper proposes a robust filtering method for quasi-accurate detection based on adaptive classification using Random Forests.Initially,we construct a Random Forest classifier utilizing multidimensional data characteristics derived from dynamic observational data collected in urban settings.This classifier facilitates the adaptive categorization of observational data into two principal classes:quasi-accurate observations and non-quasi-accurate observations.Following this classification,a novel robust positioning method for GNSS quasi-accurate detection is developed.This method leverages the quasi-accurate scores obtained during the classification process to determine the random model for quasi-accurate solutions,substituting the standardized true errors for conventional least-squares residuals in the subsequent IGG3 robustness test.Taking pseudorange differential positioning as an example,we evaluate the effectiveness of the proposed method across two positioning scenarios characterized by different magnitudes of observational errors.Experimental results demonstrate that,in comparison to traditional IGG3 and quasi-accurate detection methods,the proposed approach significantly enhances positioning performance,with accuracy improvements ranging from 16%to 51%across various testing scenarios.The random forest classification model proposed in this paper effectively improves the identification accuracy of quasi-accurate observations,and therefore can effectively improve the positioning performance in complex observation environments.In complex observation environments,the number of available GNSS satellites is significantly reduced,and the observed signals are susceptible to multidimensional outliers,resulting in challenges in o

关 键 词：GNSS 随机森林 拟准检定 粗差 定位 

分 类 号：TH-3[机械工程]