Book description
New Bayesian approach helps you solve tough problems in signal
processing with ease
Signal processing is based on this fundamental concept-the extraction
of critical information from noisy, uncertain data. Most techniques
rely on underlying Gaussian assumptions for a solution, but what
happens when these assumptions are erroneous? Bayesian techniques
circumvent this limitation by offering a completely different approach
that can easily incorporate non-Gaussian and nonlinear processes along
with all of the usual methods currently available.
This text enables readers to fully exploit the many advantages of the
"Bayesian approach" to model-based signal processing. It
clearly demonstrates the features of this powerful approach compared
to the pure statistical methods found in other texts. Readers will
discover how easily and effectively the Bayesian approach, coupled
with the hierarchy of physics-based models developed throughout, can
be applied to signal processing problems that previously seemed unsolvable.
Bayesian Signal Processing features the latest generation of
processors (particle filters) that have been enabled by the advent of
high-speed/high-throughput computers. The Bayesian approach is
uniformly developed in this book's algorithms, examples, applications,
and case studies. Throughout this book, the emphasis is on
nonlinear/non-Gaussian problems; however, some classical techniques
(e. g. Kalman filters, unscented Kalman filters, Gaussian sums,
grid-based filters, et al) are included to enable readers familiar
with those methods to draw parallels between the two approaches.
Special features include:
- Unified Bayesian treatment starting from the basics (Bayes's
rule) to the more advanced (Monte Carlo sampling), evolving to the
next-generation techniques (sequential Monte Carlo sampling)
-
Incorporates "classical" Kalman filtering for linear,
linearized, and nonlinear systems; "modern" unscented
Kalman filters; and the "next-generation" Bayesian
particle filters
-
Examples illustrate how theory can be applied directly to a
variety of processing problems
-
Case studies demonstrate how the Bayesian approach solves
real-world problems in practice
-
MATLAB notes at the end of each chapter help readers solve
complex problems using readily available software commands and
point out software packages available
-
Problem sets test readers' knowledge and help them put their
new skills into practice
The basic Bayesian approach is emphasized throughout this text in
order to enable the processor to rethink the approach to formulating
and solving signal processing problems from the Bayesian perspective.
This text brings readers from the classical methods of model-based
signal processing to the next generation of processors that will
clearly dominate the future of signal processing for years to come.
With its many illustrations demonstrating the applicability of the
Bayesian approach to real-world problems in signal processing, this
text is essential for all students, scientists, and engineers who
investigate and apply signal processing to their everyday problems.
JAMES V. CANDY, PhD, is Chief Scientist for
Engineering, founder, and former director of the Center for Advanced
Signal & Image Sciences at the Lawrence Livermore National
Laboratory. Dr. Candy is also an Adjunct Full Professor at the
University of California, Santa Barbara, a Fellow of the IEEE, and a
Fellow of the Acoustical Society of America. Dr. Candy has published
more than 225 journal articles, book chapters, and technical reports.
He is also the author of Signal Processing: Model-Based
Approach, Signal Processing: A Modern Approach, and
Model-Based Signal Processing (Wiley). Dr. Candy was awarded
the IEEE Distinguished Technical Achievement Award for his development
of model-based signal processing and the Acoustical Society of America
Helmholtz-Rayleigh Interdisciplinary Silver Medal for his
contributions to acoustical signal processing and underwater
acoustics.
