The Performance Analysis of Error Saturation Nonlinearity LMS in Impulsive Noise based on Weighted-Energy Conservation

Autor: Panigrahi, T., Ganapati Panda, Mulgrew, B.
Jazyk: angličtina
Rok vydání: 2010
Předmět:
Zdroj: Scopus-Elsevier
DOI: 10.5281/zenodo.1061711
Popis: This paper introduces a new approach for the performance analysis of adaptive filter with error saturation nonlinearity in the presence of impulsive noise. The performance analysis of adaptive filters includes both transient analysis which shows that how fast a filter learns and the steady-state analysis gives how well a filter learns. The recursive expressions for mean-square deviation(MSD) and excess mean-square error(EMSE) are derived based on weighted energy conservation arguments which provide the transient behavior of the adaptive algorithm. The steady-state analysis for co-related input regressor data is analyzed, so this approach leads to a new performance results without restricting the input regression data to be white.
{"references":["X. Wang and H. V. Poor, \"Joint channel estimation and symbol detection\nin rayleigh flat-fading channels with impulsive noise\", IEEE Commun.\nLett., vol. 1, no. 1, pp. 1921, Jan. 1997.","S. R. Kim and A. Efron, \"Adaptive robust impulsive noise filtering\", IEEE\nTrans. Signal Process., vol. 43, no. 8, pp. 1855 1866, Aug. 1995.","S. C. Chan and Y. X. Zou, \"A recursive least m-estimate algorithm\nfor robust adaptive filtering in impulsive noise: Fast algorithm and\nconvergence performance analysis\", IEEE Trans. Signal Process., vol.\n52, no. 4, pp. 975991, April 2004.","N. J. Bershad, \"On error saturation nonlinearities for LMS adaptation\",\nIEEE Trans. Acoust., Speech, Signal Process., vol. 36, no. 4, pp. 440452,\nApril 1988.","N. J. Bershad, \"On weight update saturation nonlinearities in LMS\nadaptation\", IEEE Trans. Acoust., Speech, Signal Process., vol. 38, no.\n2, pp. 623630, Feb. 1990.","H. Fan and R. Vemuri, \"Robust adaptive algorithms for active noise\nand vibration control\", Acoustics, Speech, and Signal Processing, 1990.\nICASSP-90., 1990 International Conference on, pp. 11371140 vol.2, Apr\n1990.","O. Abu-Ella and B. El-Jabu, \"Optimal robust adaptive LMS algorithm\nwithout adaptation step-size\", Millimeter Waves,2008. GSMM 2008.\nGlobal Symposium on, pp. 249251, April 2008.","N. J. Bershad and M. Bonnet, \"Saturation effects in LMS adaptive echo\ncancellation for binary data\", IEEE Trans. Signal Process., vol. 38, no.\n10, pp. 16871696, Oct. 1990.","N. J. Bershad, \"On error saturation nonlinearities for LMS adaptation\nin impulsive noise, IEEE Trans. Signal Process., vol. 56, no. 9, pp.\n45264530, Sep. 2008.\n[10] B. Widrow and S. D. Strearns, Adaptive Signal Processing. Englewood\nCliffs, NJ:Prentice-Hall, 1985.\n[11] S. Haykin, Adaptive filter theory. Englewood Cliffs, NJ:Prentice-Hall,\n2001.\n[12] A. H. Sayed, Fundamentals of Adaptive Filtering. JohnWiley and Sons.\nInc. Publication, 2003.\n[13] T. Y. Al-Naffouri and A. H. Sayed, \"Transient analysis of adaptive filters\nwith error nonlinearities\", IEEE Trans. Signal Process., vol. 51, no. 3,\npp. 653663, March 2003.\n[14] T. Y. Al-Naffouri and A. H. Sayed, \"Adaptive filters with error nonlinearities:\nMean-square analysis and optimum design\", EURASIP Journal\non Applied Signal Processing, pp. 192205, OCT. 2001.\n[15] T. Y. Al-Naffouri and A. H. Sayed, \"Transient analysis of datanormalized\nadaptive filters\", IEEE Trans. Signal Process., vol. 51, no.\n3, pp. 639652, March 2003.\n[16] R. Price, \"A usefull theorem for non-linear devices having Gaussian\ninputs\", IRE Trans. Inf. Theory, vol. IT-4, pp. 6972, June 1958.\n[17] R. Pawula, \"A modified version of prices theorem\", Information Theory,\nIEEE Transactions on, vol. 13, no. 2, pp. 285 288, Apr 1967."]}
Databáze: OpenAIRE
Externí odkaz: