Impulsive Noise-Resilient Subband Adaptive Filter
Commenced in January 2007
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Paper Count: 32799
Impulsive Noise-Resilient Subband Adaptive Filter

Authors: Young-Seok Choi

Abstract:

We present a new subband adaptive filter (R-SAF) which is robust against impulsive noise in system identification. To address the vulnerability of adaptive filters based on the L2-norm optimization criterion against impulsive noise, the R-SAF comes from the L1-norm optimization criterion with a constraint on the energy of the weight update. Minimizing L1-norm of the a posteriori error in each subband with a constraint on minimum disturbance gives rise to the robustness against the impulsive noise and the capable convergence performance. Experimental results clearly demonstrate that the proposed R-SAF outperforms the classical adaptive filtering algorithms when impulsive noise as well as background noise exist.

Keywords: Subband adaptive filter, L1-norm, system identification, robustness, impulsive interference.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1339057

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References:


[1] S. Haykin, Adaptive Filter Theory, 4th edition, Upper Saddle River, NJ: Prentice Hall, 2002.
[2] A. H. Sayed, Fundamentals of Adaptive Filtering, New York: Wiley, 2003.
[3] A. Gilloire and M. Vetterli, “Adaptive filtering in subbands with critical sampling: analysis, experiments, and application to acoustic echo cancellation,” IEEE Trans. Signal Process., vol. 40, no. 8, pp. 1862–875, Aug. 1992.
[4] S. S. Pradhan and V. U. Reddy, “A new approach to subband adaptive filtering,” IEEE Trans. Signal Process., vol. 47, no. 3, pp. 655–664, Mar. 1999.
[5] K. A. Lee and W. S. Gan, “Improving convergence of the NLMS algorithm using constrained subband updates,” IEEE Signal Processing Lett., vol. 11, no. 9, pp. 736–739, Sept. 2004.
[6] M. Shao and C. L. Nikias, “Signal processing with fractional lower order moments: Stable process and their applications,” Proc. IEEE, vol. 81, pp. 986–1010, Jul. 1993.
[7] O. Arikan, A. E. Cetin, and E. Erzin, “Adaptive filtering for non-Gaussian stable processes,” IEEE Signal Processing Lett., vol. 1, no. 11, pp. 163–165, Nov. 1994.
[8] E. Eweda, “Convergence analysis of the sign algorithm without the independence and Gaussian assumptions,” IEEE Trans. Signal Process., vol. 48, no. 9, pp. 2535–2544, Sep. 2000.
[9] T. Shao, Y. R. Zheng, J. Benesty, “An affine projection sign algorithm robust against impulsive interferences,” IEEE Signal Process. Lett., vol. 17, no. 4, pp. 327–330, 2010.
[10] D. Bertsekas, A. Nedic, and A. Ozdaglar, Convex analysis and optimization, Athena Scientific, Cambridge, USA, 2003.
[11] L. R. Vega, H. Ray, J. Benesty, and S. Tressens, “A new robust variable step-size NLMS algorithm,” IEEE Trans. Signal Process., vol. 56, no. 5, pp. 1878-1893, May 2008.