Transient analysis of data-normalized adaptive filters

Abstract: This paper develops an approach to the transient analysis of adaptive filters with data normalization. Among other results, the derivation characterizes the transient behavior of such filters in terms of a linear time-invariant state-space model. The stability, of the model then translates into the mean-square stability of the adaptive filters. Likewise, the steady-state operation of the model provides information about the mean-square deviation and mean-square error performance of the filters. In addition to deriving earlier results in a unified manner, the approach leads to stability and performance results without restricting the regression data to being Gaussian or white. The framework is based on energy-conservation arguments and does not require an explicit recursion for the covariance matrix of the weight-error vector.


Published in:
IEEE Transactions on Signal Processing, 51, 3, 639-652
Year:
2003
Publisher:
IEEE
ISSN:
1053-587X
Laboratories:




 Record created 2017-12-19, last modified 2018-09-13


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