Published April 17, 2016 | Version v1

Variable Step Size Maximum Correntropy Criteria Based Adaptive Filtering Algorithm

  • 1. Faculty of Electrical and Electronics Engineering, Sathyabama University, Chennai, India
  • 2. NEC Mobile Networks Excellence Centre Chennai, India

Description

Maximum correntropy criterion (MCC) based adaptive filters are found to be robust against impulsive interference. This paper proposes a novel MCC based adaptive filter with variable step size in order to obtain improved performance in terms of both convergence rate and steady state error with robustness against impulsive interference. The optimal variable step size is obtained by minimizing the Mean Square Deviation (MSD) error from one iteration to the other. Simulation results in the context of a highly impulsive system identification scenario show that the proposed algorithm has faster convergence and lesser steady state error than the conventional MCC based adaptive filters.

Files

ETASR_2016_04_923-926.pdf

Files (187.9 kB)

Name Size Download all
md5:5dff98ee1dc18b56d806e57dedddf9e6
187.9 kB Preview Download

Additional details

References

  • S. Haykin, Adaptive filtering theory, Prentice-Hall, New York, NY, USA, 1996.
  • J. C. Principe, Information theoretic learning: Renyi’s entropy and kernel perspectives, Springer Verlag, Berlin, Germany, 2010
  • W. Liu, P. P. Pokharel, and J. C. Principe, “Correntropy: Properties, and applications in non-Gaussian signal processing”, IEEE Trans. Signal Process., Vol. 55, No. 11, pp. 5286–5298, 2007
  • A. Singh, J. C. Principe, “Using correntropy as a cost function in adaptive filters”, International Joint Conference on Neural Networks, Atlanta, USA, pp. 2950–2955, June 14-19, 2009
  • B. Chen, L. Xing, J. Liang, N. Zheng, J. C. Príncipe, “Steady-state mean-square error analysis for adaptive filtering under the maximum correntropy criterion”, IEEE Signal Processing Letters, Vol. 21, No. 7, pp. 880-884, 2014
  • L. Shi, Y. Lin, “Convex combination of adaptive filters under the maximum correntropy criterion in impulsive interference,” IEEE Signal Processing Letters, Vol. 21, No. 11, pp. 1385-1388, 2014
  • S. Zhao, B. Chen, J. C. Principe, “An adaptive kernel width update for correntropy” , The 2012 International Joint Conference on Neural networks, Brisbane, Australia, pp. 1–5, June 10-15, 2012
  • R. H. Kwong, E. W. Johnston, “A variable step size LMS algorithm”, IEEE Transactions on Signal Processing, Vol. 40,No. 7, pp. 1633-1642, 1992
  • H. C. Shin, A. H. Sayed, W. J. Song, “Variable step-size NLMS and affine projection algorithms”, IEEE Signal Processing Letters, Vol. 11, No. 2, pp. 132-135, 2004
  • X. D. Luo, Z. H. Jia, Q. Wang, “A new variable step size LMS adaptive filtering algorithm”, Acta Electronica Sinica, Vol. 34, No. 6, pp. 1123-1126, 2006
  • A. H. Sayed, Adaptive filters, John Wiley & Sons, 2008
  • W. Ma, H. Qua, G. Gui, L. Xu, J. Zhaoa, B. Chen, “Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments”, Journal of the Franklin Institute, Vol. 352, No. 7, pp. 2708-2727, 2015