EAN: 9783642302985

SPRINGERBRIEFS IN ELECTRICAL AND COMPUTER ENGIN...

In this book  the authors provide insights into the basics of adaptive filtering  which are
particularly useful for students taking their first steps into this field. They start by
studying the problem of minimum mean-square-error filtering  i.e.  Wiener filtering. Then  they
analyze iterative methods for solving the optimization problem  e.g.  the Method of Steepest
Descent. By proposing stochastic approximations  several basic adaptive algorithms are derived
including Least Mean Squares (LMS)  Normalized Least Mean Squares (NLMS) and Sign-error
algorithms. The authors provide a general framework to study the stability and steady-state
performance of these algorithms. The affine Projection Algorithm (APA) which provides faster
convergence at the expense of computational complexity (although fast implementations can be
used) is also presented. In addition  the Least Squares (LS) method and its recursive version
(RLS)  including fast implementations are discussed. The book closes with the discussion of
several topics of interest in the adaptive filtering field.

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