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一种在紧密耦合的惯性导航系统/全球导航卫星系统（INS/GNSS）集成系统中进行故障检测与识别的新方法。

A Novel Method of Fault Detection and Identification in a Tightly Coupled, INS/GNSS-Integrated System.

作者信息

Zhang Fan, Wang Ye, Gao Yanbin

机构信息

College of Intelligent System Science and Engineering, Harbin Engineering University, Harbin 150001, China.

出版信息

Sensors (Basel). 2021 Apr 21;21(9):2922. doi: 10.3390/s21092922.

DOI:10.3390/s21092922
PMID:33919348
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8122637/
Abstract

Fault detection and identification are vital for guaranteeing the precision and reliability of tightly coupled inertial navigation system (INS)/global navigation satellite system (GNSS)-integrated navigation systems. A variance shift outlier model (VSOM) was employed to detect faults in the raw pseudo-range data in this paper. The measurements were partially excluded or included in the estimation process depending on the size of the associated shift in the variance. As an objective measure, likelihood ratio and score test statistics were used to determine whether the measurements inflated variance and were deemed to be faulty. The VSOM is appealing because the down-weighting of faulty measurements with the proper weighting factors in the analysis automatically becomes part of the estimation procedure instead of deletion. A parametric bootstrap procedure for significance assessment and multiple testing to identify faults in the VSOM is proposed. The results show that VSOM was validated through field tests, and it works well when single or multiple faults exist in GNSS measurements.

摘要

故障检测与识别对于确保紧密耦合惯性导航系统（INS）/全球导航卫星系统（GNSS）组合导航系统的精度和可靠性至关重要。本文采用方差偏移异常值模型（VSOM）来检测原始伪距数据中的故障。根据方差相关偏移的大小，测量值在估计过程中被部分排除或纳入。作为一种客观度量，似然比和得分检验统计量用于确定测量值是否使方差增大并被视为有故障。VSOM很有吸引力，因为在分析中使用适当的加权因子对有故障的测量值进行加权会自动成为估计过程的一部分，而不是删除。提出了一种用于显著性评估和多重检验以识别VSOM中故障的参数自举程序。结果表明，VSOM通过现场测试得到了验证，并且在GNSS测量中存在单个或多个故障时效果良好。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd51/8122637/751f957df8cf/sensors-21-02922-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd51/8122637/02bcfa73c88b/sensors-21-02922-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd51/8122637/8bbea027b54f/sensors-21-02922-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd51/8122637/becbc8eb592f/sensors-21-02922-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd51/8122637/c7be54b2e84b/sensors-21-02922-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd51/8122637/7b6ed8560bfa/sensors-21-02922-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd51/8122637/4577b1999458/sensors-21-02922-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd51/8122637/1e9e03aa884e/sensors-21-02922-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd51/8122637/751f957df8cf/sensors-21-02922-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd51/8122637/02bcfa73c88b/sensors-21-02922-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd51/8122637/8bbea027b54f/sensors-21-02922-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd51/8122637/becbc8eb592f/sensors-21-02922-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd51/8122637/c7be54b2e84b/sensors-21-02922-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd51/8122637/7b6ed8560bfa/sensors-21-02922-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd51/8122637/4577b1999458/sensors-21-02922-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd51/8122637/1e9e03aa884e/sensors-21-02922-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd51/8122637/751f957df8cf/sensors-21-02922-g008.jpg

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本文引用的文献

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