Abstract
An novel adaptive filtering method based on the wavelet transform is presented for a fiber optical gyroscope (FOG) on the moving base. Considering the performance difference of a FOG in different angular velocity, threshold values of different scales of wavelet coefficients are adjusted according to magnitude of FOG output signal, soft thresholding method is used to evaluate the wavelet coefficients, so effects of random signal noise and non-line of calibration factors of a FOG are removed at the maximum extent, and sensitivity of a FOG can be ensured. Filtering results of actual FOG show the proposed method has fine dynamic filtering effect.
The work was supported by the southeast university excellent young teacher foundation (4022001002) and the national defense advanced research foundation ( 6922001019 ).
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References
Hotate, K.: Optical Fiber Sensors. In: Fiber Optic Gyros, vol. IV, pp. 167–206. Artech House, Norwood (1997)
Lefevre, H.C.: The Fiber Optic Gyroscope. Artech House, Norwood (1993)
Hotate, K.: Future Evolution of Fiber Optic Gyros. In: Proc. SPIE Fiber Optic Gyros. 20th Anniversary Conf., Denver, CO, vol. 2837, pp. 33–44 (1996)
Chen, X.-Y.: Modeling Temperature Drift of FOG by Improved BP Algorithm and by Gauss-Newton Algorithm. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3174, pp. 805–812. Springer, Heidelberg (2004)
Miao, L.-J.: Application of Wavelet Analysis in the Signal Processing of the Fiber Optic Gyro. Journal of Astronautics 21(1), 42–46 (2000)
Qi, Y.-X., Gao, X.P., Yuan, R.M.: New Method for Eliminating Signal Zero Drift of Fiber Optic Gyro. Journal of Transducer Technology 22(10), 57–59 (2003)
Yuan, R.-M., Wei, X.H., Li, Z.Y.: De-noising Algorithm for Signal in FOG Based on Wavelet Filtering Using Threshold Value. Journal of Chinese Inertial Technology 11(5), 43–47 (2003)
Daubechies, I.: Ortho-normal Bases of Compactly Supported Wavelets. Communications on Pure and Applied Mathematics 41, 909–916 (1988)
Mallat, S.: A Theory for Multi-resolution Signal Decomposition: The Wavelet Representation. IEEE Transaction on Pattern Analysis and Machine Intelligence 11(7), 674–693 (1989)
Smith, M.J., Barnwell, T.P.: Exact Reconstruction for Tree-Structured Subband Coders. IEEE Transaction on Acoustics, Speech and Signal Processing 34(3), 431–441 (1986)
Strang, G., Nguyen, T.: Wavelets and Filter Banks. Wellesley-Cambridge Press, MA (1997)
Mallat, S.: A Wavelet Tour of Signal Processing. Academic Press, San Diego (1998)
Donoho, D.L.: Denoising by Soft-Thresholding. IEEE Trans. Inform. Theory 41(3), 613–627 (1995)
Fang, H.-T., Huang, D.-S., Wu, Y.-H.: Antinoise Approximation of the Lidar Signal with Wavelet Neural Networks. Applied Optics 44(6), 1077–1083 (2005)
Fang, H.T., Huang, D.-S.: Lidar Signal De-noising Based on Wavelet Trimmed Thresholding Technique. Chinese Optics Letters 2(1), 1–3 (2004)
Fang, H.T., Huang, D.-S.: Noise Reduction in Lidar Signal Based on Discrete Wavelet Transform. Optics Communications (233), 67–76 (2004)
Resnikoff, H.L., Wells, R.: Wavelet Analysis: The Scaleable Structure of Information. Springer, New York (1998)
Vidakovic, B.: Statistical Modeling by Wavelets. Wiley, New York (1999)
Jansen, M.: Noise Reduction by Wavelet Thresholding. Springer, New York (2001)
Donoho, D.L., Johnstone, I.M.: Ideal Spatial Adaptation by Wavelet Shrinkage. Biometrika (81), 425–455 (1994)
Donoho, D.L., Johnstone, I.M.: Adapting to Unknown Smoothness via Wavelet Shrinkage. J. Am. Stat. Assoc. 90(432), 1200–1224 (1995)
Stein, C.M.: Estimation of the Mean of a Multivariate Normal Distribution. Ann. Stat. 9(6), 1135–1151 (1981)
Abramovich, F., Sapatinas, T., Silverman, B.W.: Wavelet Thresholding via Bayesian Approach. J. Roy. Stat. Soc. B (60), 723–749 (1998)
Nason, G.P.: Wavelet Shrinkage Using Cross-Validation. J. Roy. Stat. Soc. B (58), 463–479 (1996)
Ye, S.: Study on Data Processing and Fusion Technology in FOG Strapdown/GPS Integrated Attitude and Heading System. Southeast University, Nanjing (2004)
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Chen, X. (2005). Adaptive Filtering Based on the Wavelet Transform for FOG on the Moving Base. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_47
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DOI: https://doi.org/10.1007/11538059_47
Publisher Name: Springer, Berlin, Heidelberg
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