Performance Enhancement of GPS/INS Integrated Navigation System Using Wavelet Based De-noising method

davari, narjes; Gholami, Asghar; shabani, mohammad

doi:10.22060/eej.2016.824

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Performance Enhancement of GPS/INS Integrated Navigation System Using Wavelet Based De-noising method

Article 5, Volume 48, Issue 2, Autumn 2016, Page 101-112 PDF (1847 K)

Document Type: Research Paper

DOI: 10.22060/eej.2016.824

Authors

narjes davari

¹; Asghar Gholami²; mohammad shabani³

¹Ph.D. Student, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran

²Associate Professor, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran

³M.Sc., Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran

Abstract

Accuracy of inertial navigation system (INS) is limited by inertial sensors imperfections. Before using inertial sensors signals in the data fusion algorithm, noise removal method should be performed, in which, wavelet decomposition method is used. In this method the raw data is decomposed into high and low frequency data sets. In this study, wavelet multi-level resolution analysis (WMRA) technique is used as an efficient pre-filter method for inertial measurements to improve the performance of INS. This technique improves navigation accuracy, eliminating high frequency noise of inertial measurements. Optimum values of the level of decomposition are selected to obtain minimum error. Successfully performing the de-noising method improves the sensors’ signal-to-noise ratios and removes short term errors mixed with motion dynamics and finally provides cleaner and more reliable data to the INS. Performance of an error state Kalman filter based GPS/INS integrated navigation system with the loosely coupled structure is studied using real measurement while GPS outages. Results show that the average value of the root mean square of the position errors using the WMRA procedure is reduced about 14% compared to those using the raw inertial measurements.

Keywords

GPS/INS Integrated Navigation; GPS Outages; Wavelet Analysis; Error State Kalman Filter; Level of Decomposition

Main Subjects

Classical Control; Signal Processing; Wavelet and its applications

Article Title [Persian]

Performance Enhancement of GPS/INS Integrated Navigation System Using Wavelet Based De-noising method

Authors [Persian]

narjes davari, Asghar Gholami, mohammad shabani

Abstract [Persian]

Keywords [Persian]

GPS/INS Integrated Navigation، GPS Outages، Wavelet Analysis، Error State Kalman Filter، Level of Decomposition

References

[1] Grewal, M. S.; Weill, L. R. and Andrews, A. P.; “Global Positioning Systems, Inertial Navigation, and Integration,” John Wiley and Sons, 2001

[2] Kalman, R. E.; “A New Approach to Linear Filtering and Prediction Problems,” ASME Trans. Ser. D: J. Basic Eng., Vol. 82, No. 1, pp. 34–45, 1960.

[3] Farrell, J. A. and Barth, M.; “The Global Positioning System and Inertial Navigation,” McGraw-Hill Professional, New York, 1999.

[4] Maybeck, P. S.; “Stochastic Models, Estimation and control,” Academic Press, New York, Vol. 1, 1979.

[5] Skog, I. and Handel, P.; “Time Synchronization Errors in Loosely Coupled GPS-Aided Inertial Navigation Systems,” IEEE Transactions on Intelligent Transportation Systems, Vol. 12, No. 4, pp. 1014–1023, 2011.

[6] Shabani, M.; Gholami, A. and Davari, N.; “Asynchronous Direct Kalman Filtering Approach for Underwater Integrated Navigation System,” Nonlinear Dyn, Vol. 80, Nos. 1–2, pp. 71–85, 2015.

[7] Wendel, J.; Schlaile, C. and Trommer, G. F.; “Direct Kalman Filtering of GPS/INS for Aerospace Applications,” International Symposium on Kinematic System in Geodesy, Geomatics and Navigation, Alberta, Canada, 2001.

[8] Qi, H. and Moore, J. B.; “Direct Kalman Filtering Approach for GPS/INS Integration,” IEEE Trans. Aerosp. Electron. Syst., Vol. 38, No. 2, pp. 687–693, 2002.

[9] Shabani, M.; Gholami, A.; Davari, N. and Emami, M.; “Implementation and Performance Comparison of Indirect Kalman Filtering Approaches for AUV Integrated Navigation System Using Low Cost IMU,” ICEE, Mashhad, Iran, pp. 1–6, 2013.

[10] Chiang, K.; Aboelmagd, N. and El-Sheimy, N.; “Constructive Neural-Networks-Based MEMS/ GPS Integration Schme,” IEEE Trans. Aerospace Electronic Syst., Vol. 44, No. 2, pp. 582–594, 2008.

[11] Schwarz, K. P. and Wei, M.; “Modeling INS/GPS for Attitude and Gravity Applications,” Proceedings of the 3rd International Workshop of High Precision Navigation, Stuttgart, Germany, Vol. 95, pp. 200– 218, 1995.

[12] Ŝkaloud, J.; “Optimizing Georeferencing of Airborne Survey Systems by INS/GPS,” Ph.D. Thesis, Department of Geomatics Engineering, University of Calgary, Calgary, Alberta, Canada, UCGE Report No. 20126, 1999.

[13] El-Rabbany, A. and El-Diasty, M.; “An Efficient Neural-Network Model for De-noising of MEMS-based Inertial Data,” The Journal of Navigation, Vol. 57, pp. 407–415, 2004.

[14] Sifuzzaman, M.; Islam, M. R. and Ali, M. Z.; “Application of Wavelet Transform and its Advantages Compared to Fourier Transform,” Journal of Physical Sciences, Vol. 13, pp. 121–134, 2009.

[15] Grejner-Brzezinska, D.; Torh, C. and Yi, Y.; “On Improving Navigation Accuracy of GPS/INS Systems,” Photogrammetric Engineering and Remote Sensing, Vol. 71, No. 4, pp. 377–389, 2005.

[16] Daubechies, I.; “Orthonormal Basis of Compactly Supported Wavelets,” Comm. Pure Applied Math., Vol. 41, No. 7, pp. 909–996, 1988.

[17] Nassar, S.; “Improving the Inertial Navigation System (INS) Error Model for INS and INS/ DGPS Applications,” Ph.D. Thesis, Department of Geomatics Engineering, University of Calgary, Canada, UCGE Report No. 20183, 2003.

[18] Abdel-Hamid, W.; Osman, A.; Noureldin, A. and El-Sheimy, N.; “Improving the Performance of MEMS-based Inertial Sensors by Removing Short- Term Errors Utilizing Wavelet Multi-Resolution Analysis,” in Proceedings of ION NTM, San Diego CA, U.S. Institute of Navigation, pp. 260–266, 2004.

[19] Bruton, A.; Schwarz, K. P. and .kaloud, J.; “The Use of Wavelets for the Analysis and De-noising of Kinematic Geodetic Measurements,” Proceedings of the IAG Symposia No. 121, Geodesy Beyond, The Challenges of the First Decade, Birmingham, UK, pp. 227–232, 2000.

[20] Zhang, T. and Xu, X. S.; “A New Method of Seamless Land Navigation for GPS/ INS Integrated System,” Measurement, Vol. 45, No. 4, pp. 691–701, 2012.

[21] Noureldin, A.; Osman, A. and EI-Sheimy, N.; “A Neuro-Wavelet Method for Multi-Sensor System Integration for Vehicular Navigation,” Meas. Sci. Technol., Vol. 15, No. 2, pp. 404–412, 2004.

[22] Burrus, C. S.; Gopinath, R. and Guo, H.; “Introduction to Wavelet and Wavelet Transforms a Primer,” Prentice Hall, 1998.

[23] Goswami, A. and Chan, K.; “Fundamentals of Wavelets: Theory, Algorithms and Applications,” Wiley, p. 359, 1999.

[24] Ogden, R. T.; “Essential Wavelets for Statistical Applications and Data Analysis,” Birkhäuser Boston, 1997.

[25] Strang, G. and Nguyen, T.; “Wavelets and Filter Banks,” Wellesley-Cambridge Press, 1997.

[26] Buccigrossi, R. W. and Simoncelli, E. P.; “Image Compression via Joint Statistical Characterization in the Wavelet Domain,” IEEE Image Process., Vol. 8, No 12, pp. 1688–1701, 1999.

[27] Romberg, J. K.; Choi, H. and Baraniuk, R. G.; “Bayesian Tree-Structured Image Modeling Using Wavelet-Domain Hidden Markov Models,” IEEE Image Process., Vol. 10, No. 7, pp. 1056–1068, 2001.

[28] Mallat, S. G.; “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” IEEE Trans Pattern Analysis and Machine Intelligence, Vol. 11, No. 7, pp. 674–693, 1989.

[29] Alnuaimy, A. N. H.; Ismail, M.; Ali, M. A. M. and Jumari, K.; “TCM and Wavelet De-noising Over an Improved Algorithm for Channel Estimations of OFDM System Based Pilot Signal,” J. Applied Sci., Vol. 9, pp. 3371–3377, 2009.

[30] Putra, T. E.; Abdullah, S.; Nuawi, M. Z. and Nopiah, Z. M.; “Wavelet Coefficient Extraction Algorithm for Extracting Fatigue Features in Variable Amplitude Fatigue Loading,” J. Applied Sci., Vol. 10, No. 4, pp. 277–283, 2010.

[31] Misiti, M.; Misiti, Y.; Oppenheim, G. and Poggi, J. M.; “Wavelet Toolbox for the Use with Matlab,” The Mathworks Inc., MA, USA, 2000.

[32] Ma, X.; Zhou, C. and Kemp, I. J.; “Automated Wavelet Selection and Thresholding for PD Detection,” IEEE Electrical Insulation Magazine, Vol. 18, pp. 37–45, 2002.

[33] Li, W. and Zhao, J.; “Wavelet Based De-noising Method to Online Measurement of Partial Discharge,” Proceedings of Asia-Pacific Power and Energy Engineering Conference, Wuhan, China, pp. 1–3, 2009.

[34] Veterli, M.; Chang, S. G. and Yu, B.; “Spatially Adaptive Wavelet Thresholding with Context Modeling for Image De-noising,” IEEE Trans. Image Process., Vol. 9, No. 9, pp. 1522–1531, 2000.

[35] Misite, M.; Misite, Y.; Oppenheim, G. and Poggi, J. M.; “Wavelet Toolbox: Computation, Visualization and Programming: Version 2,” Mathworks Inc., California, 2002.

[36] Donoho, D.; Iain, L. and Johnstone, M.; “Adapting to Unknown Smoothness via Wavelet Shrinkage,” Journal of the American Statistical Association, Vol. 90, No. 432, pp. 1200–1244, 1995.

[37] Titterton, D. H. and Weston, J. L.; “Strapdown Inertial Navigation Technology,” Peter Peregrinus, UK, 1997.

[38] Brown, R. G. and Hwang, P. Y. C.; “Introduction to Random Signals and Applied Kalman Filtering: With MATLAB Exercises and Solutions,” Wiley, New York, Vol. 1, 1997.

[39] Nassar, S. and El-Sheimy, N.; “Wavelet Analysis for Improving INS and INS/DGPS Navigation Accuracy,” The Journal of Navigation, Vol. 58, No. 1, pp. 119–134, 2005.

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