An ICA-Based Method for Improving Cross-Correlation Performance in Estimating Stress Wave Propagation Time

Abstract:

This paper presented a new propagation time estimation method based on ICA-domain filter. The technique consists of two steps. Firstly, denoising in ICA domain by sparse code shrinkage was applied to each received signal to recover the original waveform. In the process, a new shrinkage function was designed to make the filter more adaptive. Then, the propagation time was found by locating the peak of the cross-correlation function. The propagation time estimations under several conditions were simulated to evaluate the performance of the method. The experiment results show that the proposed method performs better than classic correlation methods, which makes it an efficient method in wood nondestructive evaluation.

• Periodical: Applied Mechanics and Materials (Volumes 29-32)
• Pages: 1451-1457
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.29-32.1451
• Online since: August 2010
• Authors: Yi Ming Fang, Jian Li, Hai Lin Feng
• Keywords: Independent Component Analysis (ICA), Propagation Time Estimation, Shrinkage Function, Stress Wave, Wood Nondestructive Evaluation

References

[1] J. S. Hu, C. H, Yang, W. H. Liu, An investigation of time delay estimation in room acoustic environment using magnitude ratio, , Signal Processing, vol 90, 2010, pp.378-384.

DOI: 10.1016/j.sigpro.2009.06.009

[2] S. J. Shaltaf, Neuro-fuzzy based time-delay estimation using DCT coefficients, ISA Transactions, vol 46, 2007, pp.21-30.

DOI: 10.1016/j.isatra.2006.02.002

[3] C. H. Knapp, G. C. Carter, The generalized correlation method for estimation of time delay, IEEE Trans. Acoust. Speech Signal Processing, vol 24, 1976, pp.320-327.

DOI: 10.1109/tassp.1976.1162830

[4] P. C. Ching, H. C. So, S. Q. Wu, On wavelet denoising and its applications to time delay estimation, IEEE Transactions on Signal Processing, vol 47, 1999, pp.2879-2882.

DOI: 10.1109/78.790670

[5] S. Q. Wu, H. C. So , P. C. Ching, Improvement of TDOA measurement using wavelet denoising with a novel thresholding technique, Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol 1 , 1997, pp.539-542.

DOI: 10.1109/icassp.1997.599694

[6] A. Hyvarinen, P. Hoyer, E. Oja, Image denoising by sparse code shrinkage, Intelligent Signal Processing, IEEE Press, 2001, pp.1-31.

DOI: 10.1109/ijcnn.1998.685880

[7] A. Hyvarinen, P. Hoyer, E. Oja, Sparse code shrinkage: denoising of nongaussian data by maximum likelihood estimation, Neural Computation. vol 11, 1999, pp.1739-1768.

DOI: 10.1162/089976699300016214

[8] X. H. Yu, F. M. Zhang, Z. Q. Wang, et al, Improvements on sparse coding shrinkage and contourlet transform for image denosing, Proceeding of the 5th International Conference on Wireless Communications, Networking and Mobile Computing, 2009, pp.4688-4691.

DOI: 10.1109/wicom.2009.5303004

[9] Z. Xin, P. Jancovic, L. Ju, M. Kokuer, Speech signal enhancement based on MAP algorithm in the ICA space, Signal Processing, vol 56, 2008, pp.1812-1820.

DOI: 10.1109/tsp.2007.910555

[10] A. Hyvarinen, Fast and robust fixed-point algorithms for independent component analysis, Neural Processing letters, vol 10, 1999, pp.1-5.

DOI: 10.1109/72.761722

[11] J. F. Cardoso, A. Souloumiac, Blind beamforming for nongaussian signals, IEE Proc. F, vol 140, 1993, pp.362-370.

DOI: 10.1049/ip-f-2.1993.0054

[12] A. K. Barros, A. Cichocki, Extraction of specific signals with temporal structure, Neural Computation, vol 13, 2001, p.1995-(2003).

DOI: 10.1162/089976601750399272

[13] R.D. Nowak, Optimal signal estimation using cross-validation, IEEE Signal Processing Letters, vol 3, 1996, pp.23-25.

DOI: 10.1109/97.551692