Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory
A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms. Covers important approaches to obtaining an optimal estimator and analyzing its performance; and includes numerous examples as well as applications to real- world problems. MARKETS: For practicing engineers and scientists who design and analyze signal processing systems, i.e., to extract information from noisy signals — radar...
Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory
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 processing applications, including state-of-the-art speech and communications technology as well as traditional sonar/radar systems.
Start with a quick review of the fundamental issues associated with mathematical detection, as well as the most important...
Statistical Digital Signal Processing and Modeling
The main thrust is to provide students with a solid understanding of a number of important and related advanced topics in digital signal processing such as Wiener filters, power spectrum estimation, signal modeling and adaptive filtering. Scores of worked examples illustrate fine points, compare techniques and algorithms and facilitate comprehension of fundamental concepts. Also features an abundance of interesting and challenging problems at the end of every chapter.
Statistical Signal Processing
This book embraces the many mathematical procedures that engineers and statisticians use to draw inference from imperfect or incomplete measurements. This book presents the fundamental ideas in statistical signal processing along four distinct lines: mathematical and statistical preliminaries; decision theory; estimation theory; and time series analysis.
Algorithms for Statistical Signal Processing
Keeping pace with the expanding, ever more complex applications of DSP, this authoritative presentation of computational algorithms for statistical signal processing focuses on advanced topics ignored by other books on the subject. Algorithms for Convolution and DFT. Linear Prediction and Optimum Linear Filters. Least-Squares Methods for System Modeling and Filter Design. Adaptive Filters. Recursive Least-Squares Algorithms for Array Signal Processing. QRD-Based Fast Adaptive Filter...
Digital SIgnal Processing and Statistical Classification
Statistical digital signal processing (DSP) has a wide range of applications in the areas of speech, image, video and data for the world of wireless communication, as well as in acoustics, radar, sonar, remote sensing, digital instrumentation and consumer electronics. Covering the fundamentals of this technology, this book provides a technical reference and research tool for practising engineers and for graduate students in both electrical and computer engineering. This resource introduces...
An Introduction to the Theory of Random Signals and Noise
This "bible" of a whole generation of communications engineers was originally published in 1958. The focus is on the statistical theory underlying the study of signals and noises in communications systems, emphasizing techniques as well s results. End of chapter problems are provided. Sponsored by: IEEE Communications Society
Probability, Random Variables and Stochastic Processes
The fourth edition of Probability, Random Variables and Stochastic Processes has been updated significantly from the previous edition, and it now includes co-author S. Unnikrishna Pillai of Polytechnic University. The book is intended for a senior/graduate level course in probability and is aimed at students in electrical engineering, math, and physics departments. The authors' approach is to develop the subject of probability theory and stochastic processes as a deductive discipline and to...
Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling
This book is intended for graduate students at the first year or advanced graduate level in the areas of statistical and adaptive signal processing, as well as practicing engineers. The goal of this book is to provide a unified, complete, and practical treatment of spectral estimation, signal modeling, adaptive filtering, and array processing.
The text is written in an intuitive manner and includes many illustrative examples. In addition a sufficient number of computer based experiments are...
Digital SIgnal Processing and Statistical Classification
Statistical digital signal processing (DSP) has a wide range of applications in the areas of speech, image, video and data for the world of wireless communication, as well as in acoustics, radar, sonar, remote sensing, digital instrumentation and consumer electronics. Covering the fundamentals of this technology, this book provides a technical reference and research tool for practising engineers and for graduate students in both electrical and computer engineering. This resource introduces...
Algorithms for Statistical Signal Processing
Keeping pace with the expanding, ever more complex applications of DSP, this authoritative presentation of computational algorithms for statistical signal processing focuses on advanced topics ignored by other books on the subject. Algorithms for Convolution and DFT. Linear Prediction and Optimum Linear Filters. Least-Squares Methods for System Modeling and Filter Design. Adaptive Filters. Recursive Least-Squares Algorithms for Array Signal Processing. QRD-Based Fast Adaptive Filter...
Probability, Random Variables and Stochastic Processes
The fourth edition of Probability, Random Variables and Stochastic Processes has been updated significantly from the previous edition, and it now includes co-author S. Unnikrishna Pillai of Polytechnic University. The book is intended for a senior/graduate level course in probability and is aimed at students in electrical engineering, math, and physics departments. The authors' approach is to develop the subject of probability theory and stochastic processes as a deductive discipline and to...
Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling
This book is intended for graduate students at the first year or advanced graduate level in the areas of statistical and adaptive signal processing, as well as practicing engineers. The goal of this book is to provide a unified, complete, and practical treatment of spectral estimation, signal modeling, adaptive filtering, and array processing.
The text is written in an intuitive manner and includes many illustrative examples. In addition a sufficient number of computer based experiments are...
Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory
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 processing applications, including state-of-the-art speech and communications technology as well as traditional sonar/radar systems.
Start with a quick review of the fundamental issues associated with mathematical detection, as well as the most important...
Statistical Digital Signal Processing and Modeling
The main thrust is to provide students with a solid understanding of a number of important and related advanced topics in digital signal processing such as Wiener filters, power spectrum estimation, signal modeling and adaptive filtering. Scores of worked examples illustrate fine points, compare techniques and algorithms and facilitate comprehension of fundamental concepts. Also features an abundance of interesting and challenging problems at the end of every chapter.
Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory
A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms. Covers important approaches to obtaining an optimal estimator and analyzing its performance; and includes numerous examples as well as applications to real- world problems. MARKETS: For practicing engineers and scientists who design and analyze signal processing systems, i.e., to extract information from noisy signals — radar...
Statistical Signal Processing
This book embraces the many mathematical procedures that engineers and statisticians use to draw inference from imperfect or incomplete measurements. This book presents the fundamental ideas in statistical signal processing along four distinct lines: mathematical and statistical preliminaries; decision theory; estimation theory; and time series analysis.
An Introduction to the Theory of Random Signals and Noise
This "bible" of a whole generation of communications engineers was originally published in 1958. The focus is on the statistical theory underlying the study of signals and noises in communications systems, emphasizing techniques as well s results. End of chapter problems are provided. Sponsored by: IEEE Communications Society
