Bayesian Signal Processing: Classical, Modern, and Particle Filtering Methods

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® notes at the end of each chapter help readers solved 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.

JAMES V. CANDY, PhD, is Chief Scientist for Engineering, a Distinguished Member of the Technical Staff, founder, and former director of the Center for Advanced Signal & Image Sciences at the Lawrence Livermore National Laboratory. He 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 2006). 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.