New PDF release: Adaptive Filtering

By L. Morales

ISBN-10: 9533071583

ISBN-13: 9789533071589

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In the next section, the steady-state performances for complex and real adaptive filters are derived, which are summarized in Theorem 1 based on separation principle and Theorem 2 for white Gaussian regressor, respectively. In section 3, based on Theorem 1 and Theorem 2, the steady-state performances for the real and complex least-mean p-power norm (LMP) algorithm, LMMN algorithm and their normalized algorithms, are investigated, respectively. Simulation results are given in Section 4, and conclusions are drawn in Section 5.

2009. [18] N. R. Yousef and A. H. Sayed, “Fixed-point steady-state analysis of adaptive filters,” Int. J. Contr. Signal Processing, vol. 17, pp. 237-258, 2003. [19] B. Lin, R. He, X. Wang and B. Wang, “The excess mean square error analyses for Bussgang algorithm,” IEEE Signal Processing Letters, vol. 15, pp. 793-796, 2008. [20] A. Goupil and J. Palicot, “A geometrical derivation of the excess mean square error for Bussgang algorithms in a noiseless environment”, ELSEVIER Signal Processing, vol.

2 Random signals In many real life situations, observations are made over a period of time and they are influenced by random effects, not just at a single instant but throughout the entire interval of time or sequence of times. In a “rough” sense, a random process is a phenomenon that varies to some degree unpredictably as time goes on. If we observed an entire time-sequence of the process on several different occasions, under presumably “identical” conditions, the resulting observation sequences, in general, would be different.

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Adaptive Filtering by L. Morales


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