Commenced in January 2007
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Variable Regularization Parameter Normalized Least Mean Square Adaptive Filter

Authors: Young-Seok Choi

Abstract:

We present a normalized LMS (NLMS) algorithm with robust regularization. Unlike conventional NLMS with the fixed regularization parameter, the proposed approach dynamically updates the regularization parameter. By exploiting a gradient descent direction, we derive a computationally efficient and robust update scheme for the regularization parameter. In simulation, we demonstrate the proposed algorithm outperforms conventional NLMS algorithms in terms of convergence rate and misadjustment error.

Keywords: Robustness, regularization, system identification, normalized LMS

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1112017

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References:


[1] S. Haykin, Adaptive Filter Theory, Englewood Cliffs, NJ: Prentice Hall, 2002.
[2] A. H. Sayed, Fundamentals of Adaptive Filtering, Englewood Cliffs, NJ: Prentice Hall, 2003.
[3] A. Benveniste, M. Metivier and P. Priouret, Adapive Algorithms and Stochstic Approximation, New York: Springer-Verlag, 1990
[4] J. Homer, “Detection guided NLMS estimation of sparsely parametrized channels,” IEEE Trans. Circuits Syst. II, vol. 47, No. 12, pp. 1437–1442, Dec. 2000.
[5] S. J. Park, C. G. Cho, C. Lee and D. H. Youn, “Integrated echo and noise canceler for hands-free applications,” IEEE Trans. Circuits Syst. II, vol. 49, No. 3, pp. 188–195, March 2002.
[6] D. P. Mandic, “A generalized normalized gradient descent algorithm,” IEEE Signal Processing Lett., vol. 11, No. 3, pp. 115–118, Feb. 2004.
[7] A. I. Hanna, I. Yates and D. P. Mandic, “Analysis of the class of complex-valued error adaptive normalized nonlinear gradient descent algorithms,” in Proc. IEEE Int. Conf. on Accoustics, Speech, and Signal Processing, ICASSP’03, Hong Kong, Apr. 2003, pp. II-705–708.
[8] V. Myllyl¨a and G. Schmidt, “Pseudo-optimal regulariztion for affine projection algorithms,” in Proc. IEEE Int. Conf. on Accoustics, Speech, and Signal Processing, ICASSP’02, Orlando, Florida, May 2002, pp. 1917–1920.