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A Rapid Introduction to Adaptive FilteringWiener Filtering

A Rapid Introduction to Adaptive Filtering: Wiener Filtering [Before moving to the actual adaptive filtering problem, we need to solve the optimum linear filtering problem (particularly, in the mean-square-error sense). We start by explaining the analogy between linear estimation and linear optimum filtering. We develop the principle of orthogonality, derive the Wiener–Hopf equation (whose solution lead to the optimum Wiener filter) and study the error surface. Finally, we applied the Wiener filter to the problem of linear prediction (forward and backward).] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Rapid Introduction to Adaptive FilteringWiener Filtering

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Publisher
Springer Berlin Heidelberg
Copyright
© The Author(s) 2013
ISBN
978-3-642-30298-5
Pages
7 –17
DOI
10.1007/978-3-642-30299-2_2
Publisher site
See Chapter on Publisher Site

Abstract

[Before moving to the actual adaptive filtering problem, we need to solve the optimum linear filtering problem (particularly, in the mean-square-error sense). We start by explaining the analogy between linear estimation and linear optimum filtering. We develop the principle of orthogonality, derive the Wiener–Hopf equation (whose solution lead to the optimum Wiener filter) and study the error surface. Finally, we applied the Wiener filter to the problem of linear prediction (forward and backward).]

Published: Aug 4, 2012

Keywords: Mean Square Error; Linear Prediction; Infinite Impulse Response; Wiener Filter; Finite Impulse Response Filter

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