A wavelet-based method for processing signal of fog in strap-down inertial systems

被引：0

作者：

Han, D. ^{[1
]}

Xiong, C. ^{[1
]}

Liu, H. ^{[2
]}

机构：

[1] State Key Lab of Digital Manufacturing Equipment and Technology of China, School of Mechanical Science and Engineering, Huazhong University of Science and Technology

[2] Institute of Industrial Research, University of Portsmouth, Portsmouth

来源：

International Journal of Robotics and Automation | 2009年 / 24卷 / 03期

关键词：

Filtering thresholds; Multiresolution wavelet analysis; Road irregularities; Wavelet resolution;

D O I：

10.2316/Journal.206.2009.3.206-3265

中图分类号：

学科分类号：

摘要：

Fibre optical gyroscopes (FOGs) have been applied widely in many fields in contrast with their counterparts such as mechanical gyroscopes and ring laser gyroscopes. The precision of FOG is affected significantly by bias drift, angle random walk, temperature effects and noises. Especially, uncertain disturbances resulting from road irregularities often affect accuracy of strap-down inertial system (SINS). Hence, eliminating uncertain disturbances from outputs of a FOG plays a crucial role to improve accuracy of SINS. This paper presents a wavelet-based method for denoising signals of FOGs in SINS used for exploring and rescuing robots in coal mines. Property of road irregularities in mines is taken into account as a key factor resulting in uncertain disturbances in this research. Both frequency band and amplitude of uncertain disturbances are introduced to choose filtering thresholds. Experimental results have demonstrated that the proposed method can efficiently eliminate uncertain disturbances due to road irregularities from outputs of FOGs and improve accuracy of surrogate data. It indicates that the proposed method has a significant potential in FOG-related applications.

引用

页码：185 / 193

页数：8

共 34 条

[21]

Shi Z.E., Wei G.W., Kouri D.J., Hoffman D.K., Et al., Lagrange wavelets for signal processing, IEEE Transactions on Image Processing, 10, 10, pp. 1488-1508, (2001)

[22]

Crouse M.S., Nowak R.D., Baraniuk R.G., Wavelet-based statistical signal processing using hidden Markov models, IEEE Transactions on Signal Processing, 46, 4, pp. 886-902, (1998)

[23]

Xu H.Z., Zhang D., Wavelet-based data processing for distributed fiber optic sensors, Proc. 5th Int. Conf. on Machine Learning and Cybernetics, pp. 4040-4045, (2006)

[24]

Cavalier L., Raimondo M., On choosing wavelet resolution in image deblurring, Proc. of the Int. Conf. on Computer Graphics, Imaging and Visualization, (2006)

[25]

Hall P., Nason G.P., On choosing a non-integer resolution level when using wavelet methods, Statistics and Probability Letters, 34, 1, pp. 5-11, (1997)

[26]

Hilton M.L., Ogden R.T., Data analytic wavelet threshold selection in 2-D signal denoising, IEEE Transactions on Signal Processing, 45, 2, pp. 496-500, (1997)

[27]

Han M., Liu Y.H., Xi J.H., Guo W., Noise smoothing for nonlinear time series using wavelet soft threshold, IEEE Signal Processing Letters, 14, 1, pp. 62-65, (2007)

[28]

Yan H., Cheng W., He G.M., Li G., Et al., Multiscale variational threshold SAR image denoising based on quad-tree Complex Wavelet Packets Transform, 2nd Workshop on Digital Media and Its Application in Museum & Heritage, pp. 57-62, (2007)

[29]

Reporting Vehicle Road Surface Irregularities, (1982)

[30]

Esmailzadeh E., Taghirad H.D., State-feedback control for passenger ride dynamics, Transactions of the Canadian Society for Mechanical Engineering, 19, pp. 495-508, (1995)

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