Literature DB >> 33919348

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

Fan Zhang1, Ye Wang1, Yanbin Gao1.   

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.

Entities:  

Keywords:  INS/GNSS integrated system; fault detection and identification; tightly coupled; variance shift outlier model (VSOM)

Year:  2021        PMID: 33919348     DOI: 10.3390/s21092922

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  3 in total

1.  A derivative UKF for tightly coupled INS/GPS integrated navigation.

Authors:  Gaoge Hu; Shesheng Gao; Yongmin Zhong
Journal:  ISA Trans       Date:  2014-11-10       Impact factor: 5.468

2.  A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults.

Authors:  Rui Sun; Qi Cheng; Guanyu Wang; Washington Yotto Ochieng
Journal:  Sensors (Basel)       Date:  2017-09-29       Impact factor: 3.576

3.  Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction.

Authors:  Shizhuang Wang; Xingqun Zhan; Yawei Zhai; Baoyu Liu
Journal:  Sensors (Basel)       Date:  2020-01-21       Impact factor: 3.576

  3 in total
  1 in total

1.  GAN-FDSR: GAN-Based Fault Detection and System Reconfiguration Method.

Authors:  Zihan Shen; Xiubin Zhao; Chunlei Pang; Liang Zhang
Journal:  Sensors (Basel)       Date:  2022-07-15       Impact factor: 3.847

  1 in total

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