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Hi/H∞-optimised fault detection for a surface vessel integrated navigation system

Published online by Cambridge University Press:  25 March 2022

Muzhuang Guo ,
Chen Guo  and
Chuang Zhang
Muzhuang Guo
Affiliation:
College of Marine Electrical Engineering, Dalian Maritime University, Dalian, Liaoning, China
Chen Guo*
Affiliation:
College of Marine Electrical Engineering, Dalian Maritime University, Dalian, Liaoning, China
Chuang Zhang
Affiliation:
Navigation College, Dalian Maritime University, Dalian, Liaoning, China
*
*Corresponding author. E-mail: [email protected]
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Abstract

Strapdown inertial navigation systems are widely used in surface ships and warships. Although high-precision optical fibre inertial navigation systems are available, they have high cost and limited practicality. Therefore, they cannot replace the traditional platform inertial navigation systems in all ships. Hence, microelectromechanical system (MEMS)-based inertial sensors are widely used for robust navigation. Accurate and timely identification of sensor faults while ensuring stable navigation is a challenging task. This paper proposes a robust fault detection (FD) approach for a low-cost system that loosely integrates a strapdown inertial navigation system and the global navigation satellite system, where the integrated navigation state estimation provides high-accuracy output. A cubature Hi/H∞-optimised FD filter was designed for a nonlinear discrete time-varying system considering sensitivity to faults and robustness to disturbances. Furthermore, a threshold for FD was derived considering a compromise between the false alarm rate and fault diagnosis accuracy. Finally, the proposed method was validated through simulations using multiple noise distribution sensor data generated by a ship-manoeuvring simulator.

Keywords

fault detectionsurface vessel integrated navigationHi/H∞ optimisationfault detection filterthreshold
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 3 , May 2022 , pp. 554 - 571
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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References

REFERENCES

Chiang, K. W., Tsai, G. J., Li, Y. H., Li, Y. and El-Sheimy, N. (2020). Navigation engine design for automated driving using INS/GNSS/3d LiDAR-SLAM and integrity assessment. Remote Sensing, 12(10), 1564.CrossRefGoogle Scholar
Fossen, T. I. (2011). Handbook of Marine Craft Hydrodynamics and Motion Control. Hoboken, NJ, USA: Wiley.CrossRefGoogle Scholar
Gao, G., Gao, S., Hong, G., Peng, X. and Yu, T. (2020). A robust INS/SRS/CNS integrated navigation system with the Chi-square test-based robust Kalman filter. Sensors, 20(20), 5909.CrossRefGoogle ScholarPubMed
Guo, D., Zhong, M., Ji, H., Liu, Y. and Yang, R. (2018). A hybrid feature model and deep learning based fault diagnosis for unmanned aerial vehicle sensors. Neurocomputing, 319, 155–163.CrossRefGoogle Scholar
Guo, C., Li, F., Tian, Z., Guo, W. and Tan, S. (2020). Intelligent active fault-tolerant system for multi-source integrated navigation system based on deep neural network. Neural Computing and Applications, 32(22), 16857–16874.CrossRefGoogle Scholar
IEEE. (2008). IEEE 952-1997 - IEEE Standard Specification Format Guide and Test Procedure for Single-Axis Interferometric Fiber Optic Gyros, 24 February 2003, in IEEE STD, 38–84. Institute of Electrical and Electronics Engineers.Google Scholar
IMO (2009). International Maritime Organization. International Convention for Safety of Life at Sea (SOLAS). Chapter V, pp. 262–296.Google Scholar
Jiang, W., Liu, D., Cai, B., Rizos, C., Wang, J. and Shangguan, W. (2019). A fault-tolerant tightly coupled GNSS/INS/OVS integration vehicle navigation system based on an FDP algorithm. IEEE Transactions on Vehicular Technology, 68(7), 6365–6378.CrossRefGoogle Scholar
Julier, S., Uhlmann, J. and Durrant-Whyte, H. F. (2000). A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Transactions on Automatic Control, 47(8), 1406–1409.Google Scholar
Khan, A. Q., Abid, M. and Ding, S. X. (2014). Fault detection filter design for discrete-time nonlinear systems – A mixed optimization. Systems & Control Letters, 67, 46–54.CrossRefGoogle Scholar
Li, X. and Zhou, K. (2009). A time domain approach to robust fault detection of linear time-varying systems. Automatica, 45(1), 94–102.CrossRefGoogle Scholar
Li, Y., Hou, L., Yang, Y. and Tong, J. (2020). Huber's M-estimation-based cubature Kalman filter for an INS/DVL integrated system. Mathematical Problems in Engineering, 2020.Google Scholar
Liang, Y. and Jia, Y. (2015). A nonlinear quaternion-based fault-tolerant SINS/GNSS integrated navigation method for autonomous UAVs. Aerospace Science and Technology, 40, 191–199.CrossRefGoogle Scholar
Liu, H., Zhong, M. and Yang, R. (2018). Simultaneous disturbance compensation and Hi /H∞ optimization in fault detection of UAVs. International Journal of Applied Mathematics and Computer Science, 28(2), 349–362.CrossRefGoogle Scholar
Liu, H., Zhong, M. and Liu, Y. (2019a). A new residual evaluation function based fault diagnosis for a kind of nonlinear systems. Asian Journal of Control, 21(3), 1153–1165.CrossRefGoogle Scholar
Xiong, Y., Huang, H., Guo, X., Zhang, Y. and Shen, C. (2019b). Combination of iterated cubature Kalman filter and neural networks for GPS/INS during GPS outages. Review of Scientific Instruments, 90(12), 125005.Google Scholar
Liu, D., Wang, H, Xia, Q and Jiang, C (2020). A low-cost method of improving the GNSS/SINS integrated navigation system using multiple receivers. Electron, 9(7), 1079.CrossRefGoogle Scholar
Liu, W., Liu, Y., Gunawan, B. A. and Bucknall, R. (2021). Practical moving target detection in maritime environments using fuzzy multi-sensor data fusion. International Journal of Fuzzy Systems, 1–9.Google Scholar
Majidi, M., Erfanian, A. and Khaloozadeh, H. (2020). Prediction-discrepancy based on innovative particle filter for estimating UAV true position in the presence of the GPS spoofing attacks. IET Radar, Sonar and Navigation, 14(6), 887–897.CrossRefGoogle Scholar
Ogawa, A. and Kasai, H. (1978). On the mathematical method of manoeuvring motion of ships. International Shipbuilding Progress, 25(292), 306–319.CrossRefGoogle Scholar
Qin, H., Wu, Z., Sun, Y. and Chen, H. (2019). Disturbance-observer-based prescribed performance fault-tolerant trajectory tracking control for ocean bottom flying node. IEEE Access, 7, 49004–49013.CrossRefGoogle Scholar
Song, R., Chen, X., Fang, Y. and Huang, H. (2020). Integrated navigation of GPS/INS based on fusion of recursive maximum likelihood IMM and square-root cubature Kalman filter. ISA Transactions, 105, 387–395.CrossRefGoogle ScholarPubMed
Tempo, R., Calafiore, G. and Dabbene, F. (2012). Randomized Algorithms for Analysis and Control of Uncertain Systems: With Applications. London: Springer Science+Business Media.Google Scholar
Wang, N. and Karimi, H. R. (2020). Successive waypoints tracking of an underactuated surface vehicle. IEEE Transactions on Industrial Informatics, 16(2), 898–908.CrossRefGoogle Scholar
Wang, J. L., Yang, G.-H. and Liu, J. (2007). An LMI approach to H-index and mixed $\hbox{H}_{-}/{\hbox{H}_\infty }$ fault detection observer design. Automatica, 43(9), 1656–1665.CrossRefGoogle Scholar
Wang, N., Karimi, H. R., Li, H. and Su, S. F. (2019). Accurate trajectory tracking of disturbed surface vehicles: A finite-time control approach. IEEE/ASME Transactions on Mechatronics, 24(3), 1064–1074.CrossRefGoogle Scholar
Wen, Z., Yan, G., Cai, Q. and Sun, Y. (2019). Odometer aided SINS in-motion alignment method based on backtracking scheme for large misalignment angles. IEEE Access, 8, 7937–7948.CrossRefGoogle Scholar
Xiong, H., Bian, R., Li, Y., Du, Z. and Mai, Z. (2020). Fault-tolerant GNSS/SINS/DVL/CNS integrated navigation and positioning mechanism based on adaptive information sharing factors. IEEE Systems Journal, 14(3), 3744–3754.CrossRefGoogle Scholar
Xu, H. and Lian, B. (2018). Fault detection for multi-source integrated navigation system using fully convolutional neural network. IET Radar, Sonar and Navigation, 12(7), 774–782.CrossRefGoogle Scholar
Zhang, C., Zhao, X., Pang, C., Wang, Y., Zhang, L. and Feng, B. (2020). Improved fault detection method based on robust estimation and sliding window test for INS/GNSS integration. The Journal of Navigation, 73(4), 776–796.CrossRefGoogle Scholar
Zhao, J. and Mili, L. (2019). A theoretical framework of robust H-infinity unscented Kalman filter and its application to power system dynamic state estimation. IEEE Transactions on Signal Processing, 67(10), 2734–2746.CrossRefGoogle Scholar
Zhao, L., Kang, Y., Cheng, J. and Wu, M. (2019). A fault-tolerant polar grid SINS/DVL/USBL integrated navigation algorithm based on the centralized filter and relative position measurement. Sensors, 19(18), 3899.CrossRefGoogle ScholarPubMed
Zhong, M., Ding, S. X. and Ding, E. L. (2010). Optimal fault detection for linear discrete time-varying systems. Automatica, 46(8), 1395–1400.CrossRefGoogle Scholar
Zhong, M., Liu, H. and Song, N. (2015). On designing an extended -FDF for a class of nonlinear systems. IFAC-PapersOnLine, 48(21), 707–712.CrossRefGoogle Scholar
Zhong, M., Liu, C., Zhou, D., Li, W. and Xue, T. (2019). Probability analysis of fault diagnosis performance for satellite attitude control systems. IEEE Transactions on Industrial Informatics, 15(11), 5867–5876.CrossRefGoogle Scholar
Zhong, M., Guo, J., Guo, D. and Yang, Z. (2016). An extended optimization approach to fault detection of INS/GPS-integrated system. IEEE Transactions on Instrumentation and Measurement, 65(11), 2495–2504.CrossRefGoogle Scholar
Zhong, M., Ding, S. X., Zhou, D. and He, X. (2020). An optimization approach to event-triggered fault detection for linear discrete time systems. IEEE Transactions on Automatic Control, 65(10), 4464–4471.CrossRefGoogle Scholar
Zhu, Y. and Zhou, L. (2020). A novel approach for fault detection in integrated navigation systems. IEEE Access, 8, 178954–178961.CrossRefGoogle Scholar