A unique treatment of signal processing using a model-based perspective

Signal processing is primarily aimed at extracting useful information, while rejecting the extraneous from noisy data. If signal levels are high, then basic techniques can be applied. However, low signal levels require using the underlying physics to correct the problem causing these low levels and extracting the desired information. Model-based signal processing incorporates the physical phenomena, measurements, and noise in the form of mathematical models to solve this problem. Not only does the approach...

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...