Andere Kunden interessierten sich auch für
![Channel Shortening Equalizers for UWB Rake Receivers]( "Channel Shortening Equalizers for UWB Rake Receivers")
Channel Shortening Equalizers for UWB Rake Receivers32,99 €![Assisting blind using facial recognition techniques]( "Assisting blind using facial recognition techniques")
Assisting blind using facial recognition techniques26,99 €![Blind Compensation of Non Linear Channels]( "Blind Compensation of Non Linear Channels")
Blind Compensation of Non Linear Channels32,99 €![Blind Image Forgery Detection Techniques]( "Blind Image Forgery Detection Techniques")
Blind Image Forgery Detection Techniques23,99 €![Design of Ultra Wideband Antenna Matching Networks]( "Design of Ultra Wideband Antenna Matching Networks")
Design of Ultra Wideband Antenna Matching Networks174,99 €![Blind Identification and Separation of Complex-Valued Signals]( "Blind Identification and Separation of Complex-Valued Signals")
Blind Identification and Separation of Complex-Valued Signals181,99 €![Blind Assistant Navigation System]( "Blind Assistant Navigation System")
Blind Assistant Navigation System32,99 €
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.
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.