The Influence of Satellite Configuration and Fault Duration Time on the Performance of Fault Detection in GNSS/INS Integration

For the integration of global navigation satellite system (GNSS) and inertial navigation system (INS), real-time and accurate fault detection is essential to enhance the reliability and precision of the system. Among the existing methods, the residual chi-square detection is still widely used due to its good real-time performance and sensibility of fault detection. However, further investigation on the performance of fault detection for different observational conditions and fault models is still required. In this paper, the principle of chi-square detection based on the predicted residual and least-squares residual is analyzed and the equivalence between them is deduced. Then, choosing the chi-square detection based on the predicted residual as the research object, the influence of satellite configuration and fault duration time on the performance of fault detection is analyzed in theory. The influence of satellite configuration is analyzed from the number and geometry of visible satellites. Several numerical simulations are conducted to verify the theoretical analysis. The results show that, for a single-epoch fault, the location of faulty measurement and the geometry have little effect on the performance of fault detection, while the number of visible satellites has greater influence on the fault detection performance than the geometry. For a continuous fault, the fault detection performance will decrease with the increase of fault duration time when the value of the fault is near the minimal detectable bias (MDB), and faults occurring on different satellite’s measurement will result in different detection results.

[1]  Jinling Wang,et al.  Performance Analysis of Fault Detection and Identification for Multiple Faults in GNSS and GNSS/INS Integration , 2015 .

[2]  Youlong Wu,et al.  Separability Analysis for Multiple Faults in GNSS/INS Integration , 2013 .

[3]  Rong Wang,et al.  Chi-square and SPRT combined fault detection for multisensor navigation , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[4]  W. Baarda,et al.  A testing procedure for use in geodetic networks. , 1968 .

[5]  Khashayar Khorasani,et al.  Computationally intelligent strategies for robust fault detection, isolation, and identification of mobile robots , 2016, Neurocomputing.

[6]  Jian Wang,et al.  PPP/INS tightly coupled navigation using adaptive federated filter , 2016, GPS Solutions.

[7]  Peter Teunissen,et al.  Distributional theory for the DIA method , 2017, Journal of Geodesy.

[8]  John Diesel,et al.  GPS/IRS AIME: Calculation of Thresholds and Protection Radius Using Chi-Square Methods , 1995 .

[9]  Young C. Lee,et al.  A Performance Analysis of a Tightly Coupled GPS/Inertial System for Two Integrity Monitoring Methods , 1999 .

[10]  Otmar Loffeld,et al.  INS/GPS Tightly-coupled Integration using Adaptive Unscented Particle Filter , 2010 .

[11]  Gérard Lachapelle,et al.  Development of an Integrated Low-Cost GPS/Rate Gyro System for Attitude Determination , 2004, Journal of Navigation.

[12]  Shaojun Feng,et al.  Integrity of an integrated GPS/INS system in the presence of slowly growing errors. Part I: A critical review , 2007 .

[13]  B. Brumback,et al.  A Chi-square test for fault-detection in Kalman filters , 1987 .

[14]  Li Yao,et al.  A Fast Gradual Fault Detection Method for Underwater Integrated Navigation Systems , 2016 .

[15]  Jia Guo,et al.  On Real Time Performance Evaluation of the Inertial Sensors for INS/GPS Integrated Systems , 2016, IEEE Sensors Journal.

[16]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[17]  Mathieu Joerger,et al.  Kalman Filter-Based Integrity Monitoring Against Sensor Faults , 2013 .

[18]  Jinling Wang,et al.  Extended Receiver Autonomous Integrity Monitoring (eRAIM) for GNSS/INS Integration , 2010 .

[19]  P. Konar,et al.  Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs) , 2011, Appl. Soft Comput..

[20]  Gary A. McGraw,et al.  Fault Detection and Exclusion Using Normalized Solution Separation and Residual Monitoring Methods , 2003 .

[21]  Rodney A. Walker,et al.  GPS Fault Detection with IMU and Aircraft Dynamics , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[22]  Shaojun Feng,et al.  Integrity of an integrated GPS/INS system in the presence of slowly growing errors. Part II: analysis , 2007 .

[23]  Arman Sargolzaei,et al.  Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV. , 2017, ISA transactions.

[24]  Shaojun Feng,et al.  Performance of rate detector algorithms for an integrated GPS/INS system in the presence of slowly growing error , 2012, GPS Solutions.

[25]  Chun Yang,et al.  Multi-Sensor Fusion with Interaction Multiple Model and Chi-Square Test Tolerant Filter , 2016, Sensors.

[26]  Jinsheng Zhang,et al.  Real-time fault detection method based on belief rule base for aircraft navigation system , 2013 .

[27]  Ling Yang,et al.  Optimal Fault Detection and Exclusion Applied in GNSS Positioning , 2013 .

[28]  P. J. G. Teunissen,et al.  DIA-datasnooping and identifiability , 2018, Journal of Geodesy.

[29]  Jörg F. Wagner,et al.  Applying the principle of integrated navigation systems to estimating the motion of large vehicles , 2004 .

[30]  Qi Cheng,et al.  A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults , 2017, Sensors.

[31]  Peter Teunissen,et al.  An Integrity and Quality Control Procedure for Use in Multi Sensor Integration , 1990 .

[32]  Mats A. Brenner,et al.  lntegrated GPS/lnertial Fault Detection Availability , 1995 .

[33]  Rong Wang,et al.  Approach for Detecting Soft Faults in GPS/INS Integrated Navigation based on LS-SVM and AIME , 2017, Journal of Navigation.

[34]  Lei Wang,et al.  A Novel Fault Detection Method for an Integrated Navigation System using Gaussian Process Regression , 2016, Journal of Navigation.

[35]  Ling Yang,et al.  Outlier separability analysis with a multiple alternative hypotheses test , 2013, Journal of Geodesy.