scholarly journals Model-based robust estimation and fault detection for MEMS-INS/GPS integrated navigation systems

2014 ◽  
Vol 27 (4) ◽  
pp. 947-954 ◽  
Author(s):  
Lingjuan Miao ◽  
Jing Shi
Keyword(s):  
Fault Detection ◽  
Robust Estimation ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Model Based

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

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2922
Author(s):  
Fan Zhang ◽  
Ye Wang ◽  
Yanbin Gao
Keyword(s):  
Fault Detection ◽  
Parametric Bootstrap ◽  
Integrated System ◽  
Range Data ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Multiple Faults ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Tightly Coupled

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.

A Fast Gradual Fault Detection Method for Underwater Integrated Navigation Systems

2015 ◽  
Vol 69 (1) ◽  
pp. 93-112 ◽  
Author(s):  
Liu Yi-ting ◽  
Xu Xiao-su ◽  
Liu Xi-xiang ◽  
Zhang Tao ◽  
Li Yao ◽  
...  
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
System Failure ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Detection Program ◽  
Absolute Residual ◽  
Simulation Results ◽  
Short Time

Gradual fault detection is always an important issue in integrated navigation systems, and the gradual fault is the most difficult fault to detect. To detect gradual faults in a timely and precise manner in integrated navigation systems, the statistical concepts of the normalised residual mean and the sum of absolute residuals are introduced according to the characteristics of gradual system failure in this paper. The applicability of the improved residual χ2 detection method is discussed. Then, the gradual fault detection program based on the improved residual χ2 detection method is designed with the criterion of normalised residual mean and the sum of absolute residual. The simulation results and vehicle tests show that: 1) The residual of the failed sub-system can be calculated accurately with the improved residual χ2 detection method, which has strong applicability in gradual fault detection; 2) The gradual fault can be detected in a short time by using the normalised residual mean and the sum of absolute residual.

scholarly journals A Novel Fault Detection Method for an Integrated Navigation System using Gaussian Process Regression

2016 ◽  
Vol 69 (4) ◽  
pp. 905-919 ◽  
Author(s):  
Yixian Zhu ◽  
Xianghong Cheng ◽  
Lei Wang
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Gaussian Process ◽  
Navigation System ◽  
Detection Method ◽  
Gaussian Process Regression ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Chi Squared

For the integrated navigation system, the correctness and the rapidity of fault detection for each sensor subsystem affects the accuracy of navigation. In this paper, a novel fault detection method for navigation systems is proposed based on Gaussian Process Regression (GPR). A GPR model is first used to predict the innovation of a Kalman filter. To avoid local optimisation, particle swarm optimisation is adopted to find the optimal hyper-parameters for the GPR model. The Fault Detection Function (FDF), which has an obvious jump in value when a fault occurs, is composed of the predicted innovation, the actual innovation of the Kalman filter and their variance. The fault can be detected by comparing the FDF value with a predefined threshold. In order to verify its validity, the proposed method is used in a SINS/GPS/Odometer integrated navigation system. The comparison experiments confirm that the proposed method can detect a gradual fault more quickly compared with the residual chi-squared test. Thus the navigation system with the proposed method gives more accurate outputs and its reliability is greatly improved.

Approach for Detecting Soft Faults in GPS/INS Integrated Navigation based on LS-SVM and AIME

2017 ◽  
Vol 70 (3) ◽  
pp. 561-579 ◽  
Author(s):  
Lina Zhong ◽  
Jianye Liu ◽  
Rongbing Li ◽  
Rong Wang
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Real Time ◽  
Support Vector ◽  
Observation Data ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
The Real ◽  
Time Performance ◽  
Soft Fault

In life-critical applications, the real-time detection of faults is very important in Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation systems. A new fault detection method for soft fault detection is developed in this paper with the purpose of improving real-time performance. In general, the innovation information obtained from a Kalman filter is used for test statistic calculations in Autonomous Integrity Monitored Extrapolation (AIME). However, the innovation of the Kalman filter is degraded by error tracking and closed-loop correction effects, leading to time delays in soft fault detection. Therefore, the key issue of improving real-time performance is providing accurate innovation to AIME. In this paper, the proposed algorithm incorporates Least Squares-Support Vector Machine (LS-SVM) regression theory into AIME. Because the LS-SVM has a good regression and prediction performance, the proposed method provides replaced innovation obtained from the LS-SVM driven by real-time observation data. Based on the replaced innovation, the test statistics can follow fault amplitudes more accurately; finally, the real-time performance of soft fault detection can be improved. Theoretical analysis and physical simulations demonstrate that the proposed method can effectively improve the detection instantaneity.

Magnetic compass fault detection method for GPS/INS/magnetic compass integrated navigation systems

2011 ◽  
Vol 9 (2) ◽  
pp. 276-284 ◽  
Author(s):  
Sul Gee Park ◽  
Ho Cheol Jeong ◽  
Jeong Won Kim ◽  
Dong-Hwan Hwang ◽  
Sang Jeong Lee
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Magnetic Compass ◽  
Navigation Systems ◽  
Integrated Navigation
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