Bayesian signal processing: classical, unscented and particle filtering methods

Bayesian-based signal processing is expected to dominate the future of model-based signal processing for years to come. This book develops the "Bayesian approach" to statistical signal processing for a variety of useful model sets with an emphasis on nonlinear/non-Gaussian problems, as well as classical techniques. Current applications and simple examples motivate the models and prepare the reader for developments in subsequent chapters. Although designed primarily as a graduate textbook, Bayesian Signal Processing is also useful to signal processing professionals and scientists.

Introduction. Bayesian Estimation. Simulation-Based Bayesian Methods. State-Space Models for Bayesian Processing. Classical Bayesian State-Space Processors. Modern Bayesian State-Space Processors. Particle-Based Bayesian State-Space Processors. Joint Bayesian State/Parametric Processors. Discrete Hidden Markov Model Bayesian Processors. Bayesian Processors for Physics-Based Applications.

Engineers and scientists, advanced undergraduates, graduates, and post-doctoral students