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Maximum-Likelihood Deconvolution - A Journey into Model-Based Signal Processing (Paperback, Softcover reprint of the original 1st ed. 1990)
Loot Price: R1,490
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Maximum-Likelihood Deconvolution - A Journey into Model-Based Signal Processing (Paperback, Softcover reprint of the original 1st ed. 1990)
Series: Signal Processing and Digital Filtering
Expected to ship within 10 - 15 working days
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Convolution is the most important operation that describes the
behavior of a linear time-invariant dynamical system. Deconvolution
is the unraveling of convolution. It is the inverse problem of
generating the system's input from knowledge about the system's
output and dynamics. Deconvolution requires a careful balancing of
bandwidth and signal-to-noise ratio effects. Maximum-likelihood
deconvolution (MLD) is a design procedure that handles both
effects. It draws upon ideas from Maximum Likelihood, when unknown
parameters are random. It leads to linear and nonlinear signal
processors that provide high-resolution estimates of a system's
input. All aspects of MLD are described, from first principles in
this book. The purpose of this volume is to explain MLD as simply
as possible. To do this, the entire theory of MLD is presented in
terms of a convolutional signal generating model and some
relatively simple ideas from optimization theory. Earlier
approaches to MLD, which are couched in the language of
state-variable models and estimation theory, are unnecessary to
understand the essence of MLD. MLD is a model-based signal
processing procedure, because it is based on a signal model, namely
the convolutional model. The book focuses on three aspects of MLD:
(1) specification of a probability model for the system's measured
output; (2) determination of an appropriate likelihood function;
and (3) maximization of that likelihood function. Many practical
algorithms are obtained. Computational aspects of MLD are described
in great detail. Extensive simulations are provided, including real
data applications.
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