An Efficient Hybrid Technique for Noise Reduction in Optical Gyroscope Signals

Document Type : Original Article

Authors

1 Communication Department Faculty of Electronic Engineering El-Menoufia University El-Menoufia, Egypt

2 Electrical Engineering Department Faculty of Engineering Damiatta University Damiatta, Egypt

Abstract

Gyroscopes are sensors that are used for motion measurement. They are generally used to measure rotation rate of moving equipment. There are different types of gyroscopes including mechanical, micro-electro-mechanical (MEMS) and optical gyroscopes. Gyroscope signal suffers from internal noise due to internal device operation and external noise of the environment. This paper presents a proposed hybrid technique that includes both Kalman filter and wavelet denoising. Results show the superiority of this proposed technique to the other filters. Arranging the filters in cascaded hybrid structure has an effect on the performance of the hybrid technique. Using Kalman filter as a first stage is better than using the wavelet as a first stage. For the comparison, two evaluation metrics are used: Signal-to-Noise Ratio (SNR) improvement and correlation coefficient.

Keywords


[1] X. Chen, “Adaptive filtering based on the wavelet transform for FOG on the moving base,” dvances in Intelligent Computing pp.447-455, 2005.
[2] B. Ralph, A, C. Lefevre, H, and J. Shaw, H, “An Overview of Fiber -optic Gyroscopes,” Lightwave Technology, vol. LT–2, 1984. [3] Hervé C. Lefèvre, Zhang Guicai, Wang Wei, “Optic fiber gyroscope,” Beijing: National Defense Industry Press, 2004. [4] H. C. Lefevre, “The fiber-optic gyroscope,” Artech house, 2014. [5] C. H. Hua, R. Zhang, and M. H. Zhang, “Filtering of long-term dependent fractal noise in fiber optic gyroscope,” Journal of system Engineering and Electronics 21 pp.1041-1045, 2010. [6] N. F. Song, C. X. Zhang, and X. Z. Du, “Analysis of vibration error in fiber optic gyroscope,” Conference on Advanced Sensor Systems and Applications, SPIE Proceedings Shang Hai China 4920 pp.115-121, 2002. [7] S. M. Bielas, “Stochastic and dynamic modeling of fiber gyros,” SPIE Fiber Optic and Laser Sensors XII pp.240-254, 1994. [8] H. Good wall, Chiang and N. El-Sheimy, “Positional Accuracy Enhancement of an Ins/gps Integrated System,” Coordinates magazine, vol. 2. pp.10–16, 2001. [9] A.R. Stubberud, X.H. Yu, “Signal processing for micro-inertial sensors,” in: RTO Meeting Proceedings 44, Advances in Vehicle Systems Concepts and Integration, Neuilly-Sur-Seine, France, 2000. [10] D.S Bayard, S.R Ploen, “High accuracy inertial sensors from inexpensive components,” US patent 0187623, 2003. [11] Chen Shufen, Zhu Yong, Qin Bingkun et al, “Theoretical analysis of noise on IFOG with integrated optics chip,” Optical Technique,29(3):8~10, 2003. [12] Huang Zhangyong, “Photoelectronic device and component using in optical fibre communication,” Beijing University Posts and Telecommunications Press, 2001. [13] N. El-Sheimy, Naser and Noureldin, “Wavelet Based De- Noising for IMU Alignment,” IEEE Aerospace and Electronic Systems Magazine, vol. 19, pp. 32– 39. 2004.
[14] Fuqiang Liu, Fanming Liu, Wenjing Wang and Bo Xu, “MEMS Gyro’s Output Signal Denoising Based on Wavelet Analysis,” Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, China, pp.5 – 8, August 2007. [15] D. Donoho, “Denoising by soft thresholding,” IEEE Trans. On Information Theory, Vo1.41(3). 1995. [16] LAWRENCE “The Principles of Mechanical Gyroscopes,” 1998 [17] University of Victoria. Microelectromechanical systems, 2015.
http://www.engr.uvic.ca/_mech466/MECH466-Lecture-1.pdf(lastac-cessed:January11,2016). [18] Changjoo Kim, Woon Tahk Sung, Sangkyung Sung, Sukchang Yun, and Young Jae Lee, “On the Mode- Matched Control of MEMS Vibratory Gyroscope via Phase- Domain Analysis and Design,” IEEE / ASME Transactions on Mechatronics, vol. 14, no. 4, August 2009. [19] Z. Guo, Z. Yang, L. Lin, Q. Zhao, J. Cui, X. Chi and G. Yan. “Decoupled comb capacitors for microelectromechanical tuning-fork gyroscopes,” IEEE Electron Device Letters 31(1): pp.26-28,2010. [20] S. W. Yoon, “Vibration-induced errors in MEMS tuning fork gyroscopes.” Sensors and Actuators A: Physical, pp. 32-44, 2012. [21] G. Sagnac, “L’éther lumineux démontré par l’effet du vent relatif d’éther dans un interféromètre en rotation uniforme,” Compte-renduz à l’Académie des Sciences, vol. 95, 1913, pp. 708-10.
[22] E. J. Post, Sagnac effect, “Rev. Modern Phys.,” pp. 475-94, 1967.
[23] LAWRENCE, “A.: Modern Inertial Technology,” Second Edition, Springer-Verlag New York, 1998 [24] L. Wang, Y. Hao, F. Wang, “Calibration of Low Cost MEMS Inertial Measurement Unit for an FPGA-based Navigation System,” IEEE International Conference on Information and Automation, pp. 181-186, June 2011.
[25] C. Saraporn, T. Dolwichai, J. Srisertpol and K. Teeka. “Signal Conditioning of Low-cost Gyroscope Using Kalman Filter and Nonlinear Least Square Method” Advanced Materials Research, Trans Tech Publ, (2013).
[26] H. Qian, Q. Xia, et al., “On modeling of random drift of MEMS gyroscope and design of Kalman filter,” International Conference on Mechatronics and Automation, pp. 4355-4360, August 2009.
[27] I. Daubechies, “Ten Lectures on Wavelets”, Society for Industrial and Applied Mathematics,” ISBN 0-89871-274-2, 1992. [28]http://www.ece.northwestern.edu/localapps/matlabhelp/toolbox/wavelet/ch06_a44.html
[29] S. L. Sabat, P. Rangababu, K. Karthik, G. Krishhnaprasad and J. Nayak. “System on chip implementation of 1-D Wavelet transform based denoising of Fiber Optic Gyroscope signal on FPGA,” Annual IEEE India Conference, 2011.