This book describes the essential tools and techniques of statistical signal processing. At every stage theoretical ideas are linked to specific applications in communications and signal processing using a range of carefully chosen examples. The book begins with a development of basic probability, random objects, expectation, and second order moment theory followed by a wide variety of examples of the most popular random process models and their basic uses and properties. Specific applications to the analysis of random signals and systems for communicating, estimating, detecting, modulating, and other processing of signals are interspersed throughout the book. Hundreds of homework problems are included and the book is ideal for graduate students of electrical engineering and applied mathematics. It is also a useful reference for researchers in signal processing and communications.
In An Introduction to Statistical Signal Processing with Applications, these three author/educators cover basic techniques in the processing of stochastic signals and illustrate their use in a variety of specific...
This book includes over one hundred worked problems and real world applications. Many of the examples and exercises use measured signals, most of which are from the biomedical domain.
Introduction To Statistical Signal Processing With Applications,1/e
This book introduces readers to various signal processing models that have been used in analyzing periodic data, and discusses the statistical and computational methods involved.
Introduction to Statistical Signal Processing with Applications
The most comprehensive overview of signal detection available. This is a thorough, up-to-date introduction to optimizing detection algorithms for implementation on digital computers. It focuses extensively on real-world signal...
Nowadays, many aspects of electrical and electronic engineering are essentially applications of DSP. This is due to the focus on processing information in the form of digital signals, using certain DSP hardware designed to execute software.
Bayesian inference in econometrics models using Monte Carlo integration. Econometrica, 57:1317-1339 ... Novel approach to nonlinear/non-Gaussian Bayesian state estimation. ... Numerical Bayesian methods applied to signal processing.
This is a uniquely comprehensive reference that summarizes the state of the art of signal processing theory and techniques for solving emerging problems in neuroscience, and which clearly presents new theory, algorithms, software and ...
"For those involved in the design and implementation of signal processing algorithms, this book strikes a balance between highly theoretical expositions and the more practical treatments, covering only those approaches necessary for ...