A unique treatment of signal processing using a model-based
Signal processing is primarily aimed at extracting useful
information, while rejecting the extraneous from noisy data. If
signal levels are high, then basic techniques can be applied.
However, low signal levels require using the underlying physics to
correct the problem causing these low levels and extracting the
desired information. Model-based signal processing incorporates the
physical phenomena, measurements, and noise in the form of
mathematical models to solve this problem. Not only does the
approach enable signal processors to work directly in terms of the
problem's physics, instrumentation, and uncertainties, but it
provides far superior performance over the standard techniques.
Model-based signal processing is both a modeler's as well as a
signal processor's tool.
Model-Based Signal Processing develops the model-based approach in
a unified manner and follows it through the text in the algorithms,
examples, applications, and case studies. The approach, coupled
with the hierarchy of physics-based models that the author
develops, including linear as well as nonlinear representations,
makes it a unique contribution to the field of signal processing.
The text includes parametric (e.g., autoregressive or all-pole),
sinusoidal, wave-based, and state-space models as some of the model
sets with its focus on how they may be used to solve signal
processing problems. Special features are provided that assist
readers in understanding the material and learning how to apply
their new knowledge to solving real-life problems.
* Unified treatment of well-known signal processing models
including physics-based model sets
* Simple applications demonstrate how the model-based approach
works, while detailed case studies demonstrate problem solutions in
their entirety from concept to model development, through
simulation, application to real data, and detailed performance
* Summaries provided with each chapter ensure that readers
understand the key points needed to move forward in the text as
well as MATLAB(r) Notes that describe the key commands and
toolboxes readily available to perform the algorithms
* References lead to more in-depth coverage of specialized
* Problem sets test readers' knowledge and help them put their new
skills into practice
The author demonstrates how the basic idea of model-based signal
processing is a highly effective and natural way to solve both
basic as well as complex processing problems. Designed as a
graduate-level text, this book is also essential reading for
practicing signal-processing professionals and scientists, who will
find the variety of case studies to be invaluable.
An Instructor's Manual presenting detailed solutions to all the
problems in the book is available from the Wiley editorial
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