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

Published online by Cambridge University Press:  02 February 2017

Lina Zhong ,
Jianye Liu ,
Rongbing Li  and
Rong Wang
Lina Zhong*
Affiliation:
(College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China) (Jincheng College of Nanjing University of Aeronautics and Astronautics, China)
Jianye Liu
Affiliation:
(College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China)
Rongbing Li
Affiliation:
(College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China)
Rong Wang
Affiliation:
(College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China)
*
(E-mail: zhonglina@nuaa.edu.cn)
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Abstract

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.

Keywords

GPS/INS IntegrationFault DetectionLeast Squares-Support Vector Machine (LS-SVM)Autonomous Integrity Monitored Extrapolation (AIME)

Information

Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 3 , May 2017 , pp. 561 - 579
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Bhatti, U.I. and Ochieng, W.Y. (2007b). Failure Modes and Models for Integrated GPS/INS Systems. The Journal of Navigation, 60, 327–348.Google Scholar
Bhatti, U.I., Ochieng, W.Y. and Feng, S. (2007a). Integrity of an Integrated GPS/INS System in the Presence of Slowly Growing Errors. part I: A Critical Review. GPS Solutions, 11, 173–181.Google Scholar
Bhatti, U.I., Ochieng, W.Y. and Feng, S. (2012). Performance of Rate Detector Algorithms for an Integrated GPS/INS System in the Presence of Slowly Growing Error. GPS Solutions, 16, 293–301.CrossRefGoogle Scholar
Bhavsar, H. and Panchal, M.H. (2012). A Review on Support Vector Machine for Data Classification. International Journal of Advanced Research in Computer Engineering & Technology, 1, 185–189.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, 305–316.CrossRefGoogle 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
Dandare, S.N. and Dudul, S.V. (2012). Support Vector Machine Based Multiple Fault Detection in an Automobile Engine Using Sound Signal. Journal of Electronic and Electrical Engineering, 3, 59–63.Google Scholar
Diesel, J.W. (2000). 3D AIME™ aircraft navigation, U.S. Patent No. 6,094,607. Washington, DC: U.S. Patent and Trademark Office.Google Scholar
Feng, S., Ochieng, W.Y., Walsh, D. and Ioannides, R. (2006). A Measurement Domain Receiver Autonomous Integrity Monitoring Algorithm. GPS Solutions, 10, 85–96.Google Scholar
Gross, J., Gu, Y., Gururajan, S., Seanor, B. and Napolitano, M.R. (2010). A Comparison of Extended Kalman Filter, Sigma-Point Kalman Filter, and Particle Filter in GPS/INS Sensor Fusion. Proceedings of AIAA Guidance, Navigation, and Control Conference, Toronto, Canada.CrossRefGoogle Scholar
Han, S. and Wang, J. (2010). Land Vehicle Navigation with the Integration of GPS and Reduced INS: Performance Improvement with Velocity Aiding. The Journal of Navigation, 63, 153–166.Google Scholar
Konar, P. and Chattopadhyay, P. (2011). Bearing Fault Detection of Induction Motor Using Wavelet and Support Vector Machines (SVMs). Applied Soft Computing, 11, 4203–4211.CrossRefGoogle Scholar
Lee, J.Y., Kim, H.S. and Lee, H.K. (2012). Detection of Multiple Faults in Single-Frequency Differential GPS Measurements. IET Radar, Sonar & Navigation, 6, 697–707.Google Scholar
Liu, H.Y., Feng, C.T. and Wang, H.N. (2011). Method of Inertial Aided Satellite Navigation and Its Integrity Monitoring. Journal of Astronautics, 342, 775–780.Google Scholar
Liu, H.Y., Yue, Y.Z. and Yang, Y.J. (2012). Integrity Monitoring for GNSS/Inertial Based on Multiple Solution Separation. Journal of Chinese Inertial Technology, 20, 63–68.Google Scholar
Long, B., Tian, S. and Wang, H. (2012). Feature Vector Selection Method Using Mahalanobis Distance for Diagnostics of Analog Circuits Based on LS-SVM. Journal of Electronic Testing, 28, 745–755.CrossRefGoogle Scholar
Mehrkanoon, S. and Suykens, J.A.K. (2012). LS-SVM Approximate Solution to Linear Time Varying Descriptor Systems. Automatica, 48, 2502–2511.Google Scholar
Noureldin, A., El-Shafie, A. and Bayoumi, M. (2011). GPS/INS Integration Utilizing Dynamic Neural Networks for Vehicular Navigation. Information Fusion, 12, 48–57.CrossRefGoogle Scholar
Orrù, G., Pettersson-Yeo, W., Marquand, A.F., Sartori, G. and Mechelli, A. (2012). Using Support Vector Machine to Identify Imaging Biomarkers of Neurological and Psychiatric Disease: a Critical Review. Neuroscience & Biobehavioral Reviews, 36, 1140–1152.Google Scholar
Park, S.G., Jeong, H.C., Kim, J.W., Hwang, D. and Lee, S.J. (2011). Magnetic Compass Fault Detection Method for GPS/INS/magnetic Compass Integrated Navigation Systems. International Journal of Control, Automation and Systems, 9, 276–284.CrossRefGoogle Scholar
Patino, L. and Rohmer, G. (2010). Approach for Detection and Identification of Multiple Faults in Satellite Navigation. Proceedings of the ION PLANS Conference, 221–226.Google Scholar
Schmidt, G.T. (2010). INS/GPS Technology Trends, NATO RTO Lecture Series, RTO-EN-SET-116, Low-Cost Navigation Sensors and Integration Technology, 1–24.Google Scholar
Shi, J., Miao, L., Ni, M. and Shen, J. (2012). Optimal Robust Fault-Detection Filter for Micro-Electro-Mechanical System-Based Inertial Navigation System/Global Positioning System. IET Control Theory & Applications, 6, 254–260.CrossRefGoogle Scholar
Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B. and Vandewalle, J. (2002). Least Squares Support Vector Machines. World Scientific Pub. Co., Singapore. ISBN 981-238-151-1 CrossRefGoogle Scholar
Xiong, Z., Chen, J., Wang, R. and Liu, J. (2013). A New Dynamic Vector Formed Information Sharing Algorithm in Federated Filter. Aerospace Science and Technology, 29, 37–46.Google Scholar
Xu, Z., Li, Y., Rizos, C. and Xu, X. (2010). Novel Hybrid of LS-SVM and Kalman Filter for GPS/INS Integration. Journal of Navigation, 63, 289–299.Google Scholar
Ying, Z. and Keong, K.C. (2004). Fast Leave-one-out Evaluation and Improvement on Inference for LS-SVMs. Pattern Recognition, Proceedings of the 17th International Conference on. IEEE, 3, 494–497.Google Scholar
Zhong, L.N., Li, R.B., Wang, R. and Liu, J.Y. (2011). Research on the FDI Method Based on RAIM for INS/GPS Tightly- Coupled Navigation System. Proceedings of the 2nd China Satellite Navigation Conference, Shang Hai, China.Google Scholar
Zhou, D. and Hu, Y. (2009). Fault Diagnosis Techniques for Dynamic Systems. Acta Automatica Sinica, 35, 748–758.CrossRefGoogle Scholar