Blind Identification and Separation of Complex-valued Signals / Eric Moreau, Tülay Adalı.

By:

Moreau, Eric

Contributor(s):

text

Media type:

computer

Carrier type:

online resource

ISBN:

9781118579749
1118579747
9781118579732
1118579739

Subject(s):

Genre/Form:

Electronic books.

Additional physical formats: Print version:: Blind Identification and Separation of Complex-valued Signals.LOC classification:

TK7871.99.C45 M384 2013

Online resources:

Click here to access online

Contents:

Front Matter -- Mathematical Preliminaries / Eric Moreau, Tülay Adali -- Estimation by Joint Diagonalization / Eric Moreau, Tülay Adali -- Maximum Likelihood ICA / Eric Moreau, Tülay Adali -- Bibliography -- Index.

Summary: Blind identification consists of estimating a multi-dimensional system only through the use of its output, and source separation, the blind estimation of the inverse of the system. Estimation is generally carried out using different statistics of the output. The authors of this book consider the blind identification and source separation problem in the complex-domain, where the available statistical properties are richer and include non-circularity of the sources - underlying components. They define identifiability conditions and present state-of-the-art algorithms that are based on algebraic methods as well as iterative algorithms based on maximum likelihood theory.

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eBooks

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Includes bibliographical references (pages 83-92) and index.

Blind identification consists of estimating a multi-dimensional system only through the use of its output, and source separation, the blind estimation of the inverse of the system. Estimation is generally carried out using different statistics of the output. The authors of this book consider the blind identification and source separation problem in the complex-domain, where the available statistical properties are richer and include non-circularity of the sources - underlying components. They define identifiability conditions and present state-of-the-art algorithms that are based on algebraic methods as well as iterative algorithms based on maximum likelihood theory.

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