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Improved Fault Detection Method Based on Robust Estimation and Sliding Window Test for INS/GNSS Integration

Published online by Cambridge University Press:  28 February 2020

Chuang Zhang ,
Xiubin Zhao ,
Chunlei Pang ,
Yong Wang ,
Liang Zhang  and
Bo Feng
Chuang Zhang*
Affiliation:
(Information and Navigation College, Air Force Engineering University, Xi'an, 710077, China)
Xiubin Zhao
Affiliation:
(Information and Navigation College, Air Force Engineering University, Xi'an, 710077, China)
Chunlei Pang
Affiliation:
(Information and Navigation College, Air Force Engineering University, Xi'an, 710077, China)
Yong Wang
Affiliation:
(Information and Navigation College, Air Force Engineering University, Xi'an, 710077, China)
Liang Zhang
Affiliation:
(Information and Navigation College, Air Force Engineering University, Xi'an, 710077, China)
Bo Feng
Affiliation:
(Information and Navigation College, Air Force Engineering University, Xi'an, 710077, China)
*
(E-mail: zhangchuanglw@163.com)
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Abstract

Real-time and accurate fault detection and isolation is very important to ensure the reliability and precision of integrated inertial navigation and global navigation satellite systems. In this paper, the detection performance of a residual chi-square method is analysed, and on this basis an improved method of fault detection is proposed. The local test based on a standardised residual is introduced to detect and identify faulty measurements directly. Differing from the traditional method, two appropriate thresholds are selected to calculate the weight factor of each measurement, and the gain matrix is adjusted adaptively to reduce the influence of the undetected faulty measurement. The sliding window test, which uses past measurements, is also added to further improve the fault detection performance for small faults when the local test based on current measurements cannot judge whether a fault has occurred or not. Several simulations are conducted to evaluate the proposed method. The results show that the improved method has better fault detection performance than the traditional detection method, especially for small faults, and can improve the reliability and precision of the navigation system effectively.

Keywords

INS/GNSS IntegrationFault DetectionRobust EstimationSliding Window Test

Information

Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 4 , July 2020 , pp. 776 - 796
Copyright
Copyright © The Royal Institute of Navigation 2020

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References

REFERENCES

Abuhashim, T. S., Abdel-Hafez, M. F. and Al-Jarrah, M. A. (2010). Building a robust integrity monitoring algorithm for a low cost GPS-aided-INS system. International Journal of Control, Automation and Systems, 8(5), 1108–1122.10.1007/s12555-010-0520-1CrossRefGoogle Scholar
Alqurashi, M. and Wang, J. L. (2015). Performance analysis of fault detection and identification for multiple faults in GNSS and GNSS/INS integration. Journal of Applied Geodesy, 9(1), 35–48.10.1515/jag-2014-0019CrossRefGoogle Scholar
Bhatti, U. I. and Ochieng, W. Y. (2007). Failure modes and models for integrated GPS/INS systems. The Journal of Navigation, 60(2), 327–348.10.1017/S0373463307004237CrossRefGoogle Scholar
Bhatti, U. I., Ochieng, W. Y. and Feng, S. J. (2007). Integrity of an integrated GPS/INS system in the presence of slowly growing errors. Part II: analysis. GPS Solutions, 11(3), 183–192.10.1007/s10291-006-0049-1CrossRefGoogle Scholar
Bhatti, U. I., Ochieng, W. Y. and Feng, S. J. (2012). Performance of rate detector algorithms for an integrated GPS/INS system in the presence of slowly growing error. GPS Solutions, 16(3), 293–301.10.1007/s10291-011-0231-yCrossRefGoogle Scholar
Brenner, M. (1995). Integrated GPS Inertial Fault Detection Availability. Proceedings of the Institute of Navigation's 8th International Technical Meeting, Palm Springs, CA, 1949–1958.Google Scholar
Bruggemann, T. S., Greer, D. G. and Walker, R. A. (2011). GPS fault detection with IMU and aircraft dynamics. IEEE Transactions on Aerospace and Electronic Systems, 47(1), 305–316.10.1109/TAES.2011.5705677CrossRefGoogle Scholar
Brumback, B. D. and Srinath, M. A. (1987). Chi-square test for fault-detection in Kalman filter. IEEE Transactions on Automatic Control, 32(6), 552–554.10.1109/TAC.1987.1104658CrossRefGoogle Scholar
Chang, L. B., Hua, B. Q. and Chang, G. B. (2013). Robust derivative-free Kalman filter based on Huber's M-estimation methodology. Journal of Process Control, 23(10), 1555–1561.10.1016/j.jprocont.2013.05.004CrossRefGoogle Scholar
Chen, C. X., Wang, X. J., Niu, D., Ren, X. Y. and Qu, K. (2014). A hierarchical fault detection method based on LS-SVM in integrated navigation system. Sensors & Transducers, 175, 111–116.Google Scholar
Call, C., Ibis, M., McDonald, J., and Vanderwerf, K,. (2006). Performance of Honeywell's Inertial/GPS Hybrid (HIGH) for RNP Operations. Proceedings of IEEE/ION PLANS, San Diego, CA, USA, 244–255.Google Scholar
Diesel, J. W. and Luu, S. (1995). GPS/IRS AIME: Calculation of Thresholds and Protection Radius Using Chi-Square Methods. Proceedings of the Institute of Navigation's 8th International Technical Meeting, Palm Springs, CA, 1959–1964.Google Scholar
Gandhi, M. A. and Mili, L. (2010). Robust Kalman filter based on a generalized maximum-likelihood-type estimator. IEEE Transactions on Signal Processing, 58(5), 2509–2520.10.1109/TSP.2009.2039731CrossRefGoogle Scholar
Gautier, J. D. and Parkinson, B. W. (2003). Using the GPS/INS Generalized Evaluation Tool (Comparison of Loosely-Coupled, Tightly-Coupled and Ultra-Tightly Coupled Integration Navigation). Proceedings of ION annual meeting, Albuquerque, NM, 65–76.Google Scholar
Hewitson, S. and Wang, J. L. (2010). Extended receiver autonomous integrity monitoring (eRAIM) for GNSS/INS integration. Journal of Surveying Engineering, 136(1), 85–101.10.1061/(ASCE)0733-9453(2010)136:1(13)CrossRefGoogle Scholar
Hu, G. G., Gao, S. S. and Zhong, Y. M. (2015). A derivative UKF for tightly coupled INS/GPS integrated navigation. ISA Transactions, 56, 135–144.10.1016/j.isatra.2014.10.006CrossRefGoogle ScholarPubMed
Jiang, C., Zhang, S. B. and Zhang, Q. Z. (2016). A new adaptive H-infinity filtering algorithm for the GPS/INS integrated navigation. Sensors, 16(12), 2127–2142.10.3390/s16122127CrossRefGoogle ScholarPubMed
Jiang, W., Li, Y. and Rizos, C. (2017). A multisensor navigation system based on an adaptive fault-tolerant GOF algorithm. IEEE Transaction on Intelligent Transportation Systems, 18(1), 103–113.10.1109/TITS.2016.2562700CrossRefGoogle Scholar
Joerger, M. and Pervan, B. (2013). Kalman filter-based integrity monitoring against sensor faults. Journal of Guidance Control and Dynamics, 36(2), 349–361.10.2514/1.59480CrossRefGoogle Scholar
Kelly, R. J. (1998). The linear model, RNP, and the near-optimum fault detection and exclusion algorithm. The Journal of Navigation, 51(5), 227–260.Google Scholar
Lee, Y. C. and O'Laughlin, D. G. (2000). A performance analysis of a tightly coupled GPS/Inertial system for two integrity monitoring methods. The Journal of Navigation, 47(3), 175–189.10.1002/j.2161-4296.2000.tb00212.xCrossRefGoogle Scholar
Li, Z. K., Gao, J. X., Wang, J. and Yao, Y. F. (2017). PPP/INS tightly coupled navigation using adaptive federated filter. GPS Solutions, 21(1), 137–148.10.1007/s10291-015-0511-zCrossRefGoogle Scholar
Liu, Y. T., Xu, X. S., Liu, X. X., Zhang, T., Li, Y., Yao, Y. Q., Wu, L. and Tong, J. W. (2016). A fast gradual fault detection method for underwater integrated navigation systems. The Journal of Navigation, 69(1), 93–112.Google Scholar
Sun, R., Cheng, Q., Wang, G. Y. and Ochieng, W. Y. (2017). A novel online data-driven algorithm for detecting UAV navigation sensor faults. Sensors, 17(10), 2243–2254.10.3390/s17102243CrossRefGoogle ScholarPubMed
Teunissen, P. J. G. (1990). An Integrity and Quality Control Procedure for use in Multi Sensor Integration. Proceedings of 3rd International Technical Meeting of the Satellite Division of the Institute of Navigation, Colorado Spring, CO, 513–522.Google Scholar
Teunissen, P. J. G. (2017). Distributional theory for the DIA method. Journal of Geodesy, 91(1), 1–22.Google Scholar
Wang, C. C., Lachapelle, G. and Cannon, M. E. (2004). Development of an integrated low-cost GPS/rate gyro system for attitude determination. The Journal of Navigation, 57(1), 85–101.10.1017/S0373463303002583CrossRefGoogle Scholar
Yang, Y. X. and Gao, W. G. (2005). Comparison of adaptive factors in Kalman filters on navigation results. The Journal of Navigation, 58(3), 471–478.10.1017/S0373463305003292CrossRefGoogle Scholar
Yang, Y. X., He, H. and Xu, G. (2001). Adaptively robust filtering for kinematic geodetic positioning. Journal of Geodesy, 75(2), 109–116.10.1007/s001900000157CrossRefGoogle Scholar
Yang, L., Knight, N. L., Li, Y. and Rizos, C. (2013). Optimal fault detection and exclusion applied in GNSS positioning. The Journal of Navigation, 66(5), 683–700.10.1017/S0373463313000155CrossRefGoogle Scholar
Yang, C., Mohammadi, A. and Chen, Q. W. (2016). Multi-sensor fusion with interaction multiple model and chi-square test tolerant filter. Sensors, 16(11), 1835–1859.10.3390/s16111835CrossRefGoogle ScholarPubMed
Zhao, X., Wang, S. C., Zhang, J. S., Fan, Z. L. and Min, H. B. (2013). Real-time fault detection method based on belief rule base for aircraft navigation system. Chinese Journal of Aeronautics, 26(3), 717–729.10.1016/j.cja.2013.04.039CrossRefGoogle Scholar
Zhong, M. Y., Guo, J. and Yang, Z. H. (2016). On real time performance evaluation of the inertial sensors for INS/GPS integrated systems. IEEE Sensors Journal, 16(17), 6652–6661.10.1109/JSEN.2016.2588140CrossRefGoogle Scholar
Zhong, L. N., Liu, J. Y., Li, R. B. and Wang, R. (2017). Approach for detecting soft faults in GPS/INS integrated navigation based on LS-SVM and AIME. The Journal of Navigation, 70(3), 561–579.10.1017/S037346331600076XCrossRefGoogle Scholar
Zhu, Y. X., Cheng, X. H. and Wang, L. (2016). A novel fault detection method for an integrated navigation system using Gaussian process regression. The Journal of Navigation, 69(4), 905–919.CrossRefGoogle Scholar