Bayesian Signal Processing
Classical, Modern and Particle Filtering Methods
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Beschreibung
New Bayesian approach helps you solve tough problems in signalprocessing with ease
Signal processing is based on this fundamental concept--theextraction of critical information from noisy, uncertain data. Mosttechniques rely on underlying Gaussian assumptions for a solution,but what happens when these assumptions are erroneous? Bayesiantechniques circumvent this limitation by offering a completelydifferent approach that can easily incorporate non-Gaussian andnonlinear processes along with all of the usual methods currentlyavailable.
This text enables readers to fully exploit the many advantagesof the "Bayesian approach" to model-based signal processing. Itclearly demonstrates the features of this powerful approachcompared to the pure statistical methods found in other texts.Readers will discover how easily and effectively the Bayesianapproach, coupled with the hierarchy of physics-based modelsdeveloped throughout, can be applied to signal processing problemsthat previously seemed unsolvable.
Bayesian Signal Processing features the latest generationof processors (particle filters) that have been enabled by theadvent of high-speed/high-throughput computers. The Bayesianapproach is uniformly developed in this book's algorithms,examples, applications, and case studies. Throughout this book, theemphasis is on nonlinear/non-Gaussian problems; however, someclassical techniques (e.g. Kalman filters, unscented Kalmanfilters, Gaussian sums, grid-based filters, et al) are included toenable readers familiar with those methods to draw parallelsbetween the two approaches.
Special features include:
* Unified Bayesian treatment starting from the basics (Bayes'srule) to the more advanced (Monte Carlo sampling), evolving to thenext-generation techniques (sequential Monte Carlo sampling)
* Incorporates "classical" Kalman filtering for linear,linearized, and nonlinear systems; "modern" unscented Kalmanfilters; and the "next-generation" Bayesian particle filters
* Examples illustrate how theory can be applied directly to avariety of processing problems
* Case studies demonstrate how the Bayesian approach solvesreal-world problems in practice
* MATLAB notes at the end of each chapter help readers solvecomplex problems using readily available software commands andpoint out software packages available
* Problem sets test readers' knowledge and help them put their newskills into practice
The basic Bayesian approach is emphasized throughout this textin order to enable the processor to rethink the approach toformulating and solving signal processing problems from theBayesian perspective. This text brings readers from the classicalmethods of model-based signal processing to the next generation ofprocessors that will clearly dominate the future of signalprocessing for years to come. With its many illustrationsdemonstrating the applicability of the Bayesian approach toreal-world problems in signal processing, this text is essentialfor all students, scientists, and engineers who investigate andapply signal processing to their everyday problems.
Autor*in
James V. Candy
Themen in »Bayesian Signal Processing«
Electrical & Electronics Engineering Elektrotechnik u. Elektronik Engineering Statistics Numerical Methods & Algorithms Numerische Methoden u. Algorithmen Signal Processing Signalverarbeitung Statistics Statistik Statistik in den Ingenieurwissenschaften Technische Statistik