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Blind Identification And Separation Of Complex-Valued Signals

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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





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    About the Authors Eric Moreau is Professor of Electrical Engineering at the University of Toulon, France

    Blind identification consists of estimating a multidimensional system through the use of only its output

    Contents 1

    Estimation by Joint Diagonalization

    Estimation is generally carried out using different statistics of the output

    Her research interests concern statistical and adaptive signal processing, with an emphasis on nonlinear and complex-valued signal processing, and applications in biomedical data analysis and communications

    His research interests concern statistical signal processing, high order statistics and matrix/tensor decompositions with applications to data analysis, telecommunications and radar

    Mathematical Preliminaries

    Maximum Likelihood ICA

    Source separation is concerned with the blind estimation of the inverse of the system

    The algorithms depend critically on statistical and/or time frequency properties of complex sources that will be precisely described.

    The authors consider the blind estimation of a multiple input/multiple output MIMO system that mixes a number of underlying signals of interest called sources

    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

    The estimation is generally performed by using different statistics of the outputs

    They also consider the case of direct estimation of the inverse system for the purpose of source separation

    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

    They then describe the estimation theory associated with the identifiability conditions and dedicated algebraic algorithms

    T lay Adali is Professor of Electrical Engineering and Director of the Machine Learning for Signal Processing Laboratory at the University of Maryland, Baltimore County, USA

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