Bayesian signal processing: classical, modern and particle filtering methods

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 parallelbetween the approaches. The common ground is the model sets. This text bringsthe 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.

• ISBN: 978-0-470-18094-5
• Editorial: John Wiley & Sons
• Encuadernacion: Cartoné
• Páginas: 445
• Fecha Publicación: 23/04/2009
• Nº Volúmenes: 1
• Idioma: Inglés