Описание: This book presents a unique viewpoint of signal processing from the Bayesian perspective in contrast to the pure statistical approach found in many textbooks. It features the next generation of processors that have recently been enabled with the advent of high speed/high throughput computers. The emphasis is on nonlinear/non-Gaussian problems, but classical techniques are included as special cases to enable the reader familiar with such methods to draw a parallel between the approaches. The common ground is the model sets. This text brings the reader from the classical methods of model-based signal processing including Kalman filtering for linear, linearized and approximate nonlinear processors as well as the recently developed unscented or sigma-point filters to the next generation of processors that will clearly dominate the future of model-based signal processing for years to come. Current applications (e.g. structures, tracking, equalization, biomedical) and simple examples to motivate the organization of the text are discussed. Examples are given to motivate all of the models and prepare the reader for further developments in subsequent chapters. In each case the processor along with accompanying simulations are discussed and applied to various data sets demonstrating the applicability and power of the Bayesian approach. The proposed text will be linked to the MATLAB (signal processing standard software) software package providing Notes as well as simple coding examples for illustrative purposes.
Автор: Steve Winder Название: Analog and Digital Filter Design, ISBN: 0750675470 ISBN-13(EAN): 9780750675475 Издательство: Elsevier Science Рейтинг: Цена: 11930.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides a practical design guide to design effective and working electronic filters. This book contains questions at the end of each chapter and includes electronic simulation tools. It also presents background information and equations, and keeps heavy mathematics to a minimum.
Presents the Bayesian approach to statistical signal processing for a variety of useful model sets
This book aims to give readers a unified Bayesian treatment starting from the basics (Baye's rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on "Sequential Bayesian Detection," a new section on "Ensemble Kalman Filters" as well as an expansion of Case Studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real-world problems incorporating detailed particle filter designs, adaptive particle filters and sequential Bayesian detectors. In addition to these major developments a variety of sections are expanded to "fill-in-the gaps" of the first edition. Here metrics for particle filter (PF) designs with emphasis on classical "sanity testing" lead to ensemble techniques as a basic requirement for performance analysis. The expansion of information theory metrics and their application to PF designs is fully developed and applied. These expansions of the book have been updated to provide a more cohesive discussion of Bayesian processing with examples and applications enabling the comprehension of alternative approaches to solving estimation/detection problems.
The second edition of Bayesian Signal Processing features
"Classical" Kalman filtering for linear, linearized, and nonlinear systems; "modern" unscented and ensemble Kalman filters: and the "next-generation" Bayesian particle filters
Sequential Bayesian detection techniques incorporating model-based schemes for a variety of real-world problems
Practical Bayesian processor designs including comprehensive methods of performance analysis ranging from simple sanity testing and ensemble techniques to sophisticated information metrics
New case studies on adaptive particle filtering and sequential Bayesian detection are covered detailing more Bayesian approaches to applied problem solving
MATLAB(R) notes at the end of each chapter help readers solve complex problems using readily available software commands and point out other software packages available
Problem sets included to test readers' knowledge and help them put their new skills into practice Bayesian
Signal Processing, Second Edition is written for all students, scientists, and engineers who investigate and apply signal processing to their everyday problems.
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