Adaptive Filtering by L. Morales PDF
By L. Morales
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Additional resources for Adaptive Filtering
1853-1864, May, 2008.  B. Lin, R. He, L. Song and B. Wang, “Steady-state performance analysis for adaptive filters with error nonlinearities,” Proc. of ICASSP, Taibei, Taiwan, pp. , 2009.  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.  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.
Here, the function randn is used to generate the normally distributed (Gaussian) sequence with zero mean and unit covariance in Matlab software, and rand is used to generate the uniformly distributed sequence. The regressors u i are generated as the following two models. 1 The regressors u i are generated as independent realizations of a Gaussian distribution with a covariance matrix R u (a diagonal unit matrix). d. Gaussian random process. 8 . 2 MSE and tracking performance simulation Fig.
Observing from the Table 1 in , we can see that this expression holds true only for the adaptive filters with most kinds of the preconditioning input data, and can not be used to analyze the adaptive filters with error nonlinearities. These points motivate the development in this paper of a unified approach to get their general expressions for the steady-state performance of adaptive filters. In our analyses, second-order TSE will be used to analyze the performance for adaptive algorithms for real-valued cases.
Adaptive Filtering by L. Morales