Blind Equalizers By Techniques Of Optimal Non-Linear Filtering Theory

Blind deconvolution, fundamental in signal processing
applications, is a challenging problem. Although
blind equalization is essentially a nonlinear
filtering problem, it has not yet been treated by all
the well known advanced techniques of optimal
non-linear filtering theory. In this book we use the
Edgeworth expansion, maximum entropy argumentations
and the Laplace integral method in order to obtain
two new groups of blind deconvolution methods which
outperform the old and new algorithms. The proposed
methods are based on new, closed formed approximated
expressions for the conditional expectation suitable
for the blind deconvolution problem. Since these
expressions do not impose any restrictions (except
that of even symmetric) on the probability
distribution of the (unobserved) input sequence, they
are suitable for a wider range of source pdf compared
to Bellini's, Fiori's or Haykin's expression. The
derivation of the above mentioned equalizers are
accompanied by theoretical analysis of the
performance in the mean square error (MSE) sense and
are justified via simulation. These new methods are
useful to professionals in Communications who seek
for improved equalization methods.

Autorentext

Monika Pinchas, PhD: Studied Electrical Engineering at Tel-Aviv
University. Lecturer at Ariel University Center of Samaria
Israel. Research interests: Blind deconvolution/equalization and
synchronization. In the past served as the CTO at Resolute
Networks. Included in the 10th Anniversary Edition of "Marquis
Who's Who in Science and Engineering.

Klappentext

Blind deconvolution, fundamental in signal processing
applications, is a challenging problem. Although
blind equalization is essentially a nonlinear
filtering problem, it has not yet been treated by all
the well known advanced techniques of optimal
non-linear filtering theory. In this book we use the
Edgeworth expansion, maximum entropy argumentations
and the Laplace integral method in order to obtain
two new groups of blind deconvolution methods which
outperform the old and new algorithms. The proposed
methods are based on new, closed formed approximated
expressions for the conditional expectation suitable
for the blind deconvolution problem. Since these
expressions do not impose any restrictions (except
that of even symmetric) on the probability
distribution of the (unobserved) input sequence, they
are suitable for a wider range of source pdf compared
to Bellini's, Fiori's or Haykin's expression. The
derivation of the above mentioned equalizers are
accompanied by theoretical analysis of the
performance in the mean square error (MSE) sense and
are justified via simulation. These new methods are
useful to professionals in Communications who seek
for improved equalization methods.