Wiener Filtering

Chapter
Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

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).

Keywords

Mean Square Error Linear Prediction Infinite Impulse Response Wiener Filter Finite Impulse Response Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    A.H. Sayed, Adaptive Filters (John Wiley& Sons, Hoboken, 2008)Google Scholar
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    S. Haykin, Adaptive Filter Theory, 4th edn. (Prentice-Hall, Upper Saddle River, 2002)Google Scholar
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    G.H. Golub, C.F. van Loan, Matrix Computations (The John Hopkins University Press, Baltimore, 1996)Google Scholar
  4. 4.
    B.D.O. Anderson, J.B. Moore, Optimal Filtering (Prentice-Hall, Englewood Cliffs, 1979)Google Scholar
  5. 5.
    T. Kailath, A.H. Sayed, B. Hassibi, Linear estimation (Prentice-Hall, Upper Saddle River, 2000)Google Scholar

Copyright information

© The Author(s) 2013

Authors and Affiliations

  1. 1.School of EngineeringUniversity of Buenos AiresBuenos AiresArgentina
  2. 2.Department of EngineeringUniversity of LeicesterLeicesterUK

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