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Algorithms for Statistical Signal Processing, by John G. Proakis, Charles M. Rader, Fuyun Ling, Marc Moonen, Ian K. Proudler, Chrysostomos
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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 Algorithms. Power Spectrum Estimation. Signal Analysis with Higher-Order Spectra. For Electrical Engineers, Computer Engineers, Computer Scientists, and Applied Mathematicians.
- Sales Rank: #2228873 in Books
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- Published on: 2002-01-15
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- Original language: English
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- 564 pages
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From the Back Cover
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 Algorithms. Power Spectrum Estimation. Signal Analysis with Higher-Order Spectra. For Electrical Engineers, Computer Engineers, Computer Scientists, and Applied Mathematicians.
Excerpt. � Reprinted by permission. All rights reserved.
The field of digital signal processing (DSP) has expanded rapidly over the past three decades. During the late sixties and seventies, we witnessed the development of the basic theory for digital filter design and the development of computationally efficient algorithms for evaluating the Fourier transform, convolution, and correlation. During the past two decades, we experienced an explosion in DSP applications spurred by significant advances in digital computer technology and integrated-circuit fabrication. In this period, the basic DSP theory has expanded to include parametric signal modeling, with applications to power spectrum estimation and system modeling, adaptive signal processing algorithms, multirate and multidimensional signal processing, and higher-order statistical methods for signal processing.
With the expansion of basic DSP theory and the rapid growth in applications (spurred by the development of fast and inexpensive digital signal processors), there is a growing interest in advanced courses in DSP covering a variety of topics. This book was written with the goal of satisfying, in part, the resulting need for textbooks covering these advanced topics.
Most of the material contained in this book was first published in 1992 by the Macmillan Publishing Company, in a book entitled Advanced Digital Signal Processing (which went out of print in 1997). This new book differs from the earlier publication by the inclusion of a new chapter (Chapter 7) on QRD-based fast adaptive filter algorithms, and the deletion of a chapter on multirate signal processing. The other chapters have remained essentially the same.
The major focus of this book is on algorithms for statistical signal processing. Chapter 2 treats computationally efficient algorithms for convolution and for the computation of the discrete Fourier transform. Chapter 3 treats linear prediction and optimum Wiener filters; included in this chapter is a description of the Levinson-Durbin and Schur algorithms. Chapter 4 considers the filter design problem based on the least-squares method and describes several methods for solving least squares problems, including the Givens transformation, the Householder transformation, and singular-value decomposition. Chapter 5 treats single-channel adaptive filters based on the LMS algorithm and on recursive least-squares algorithms. Chapter 6 describes computationally efficient recursive least-squares algorithms for multichannel signals. Chapter 7 is focused on the uses of signal flow graphs for deriving computationally efficient adaptive filter algorithms based on the QR decomposition. Chapter 8 deals with power spectrum estimation, including both parametric and nonparametric methods. Chapter 9 describes the use of higher-order statistical methods for signal modeling and system identification.
Although the material in this book was written by six different authors, we have tried very hard to maintain common notation throughout the book. We believe we have succeeded in developing a coherent treatment of the major topics outlined in the preceding overview. Chapter 1 provides an introduction to selected basic DSP material that is typically found in a first-level DSP text, and also serves to establish some of the notation used throughout the book.
In our treatment of the various topics covered herein, we generally assume that the reader has had a prior course on the fundamentals of digital signal processing. The fundamental topics assumed as background include the z-transform, the analysis and characterization of discrete-time systems, the Fourier transform, the discrete Fourier transform (DFT), and the design of FIR and IIR digital filters. John G. Proakis
Charles M. Rader Fuyun Ling
Chrysostomos L. Nikias
Marc Moonen
Ian K Proudler
Excerpt. � Reprinted by permission. All rights reserved.
The field of digital signal processing (DSP) has expanded rapidly over the past three decades. During the late sixties and seventies, we witnessed the development of the basic theory for digital filter design and the development of computationally efficient algorithms for evaluating the Fourier transform, convolution, and correlation. During the past two decades, we experienced an explosion in DSP applications spurred by significant advances in digital computer technology and integrated-circuit fabrication. In this period, the basic DSP theory has expanded to include parametric signal modeling, with applications to power spectrum estimation and system modeling, adaptive signal processing algorithms, multirate and multidimensional signal processing, and higher-order statistical methods for signal processing.
With the expansion of basic DSP theory and the rapid growth in applications (spurred by the development of fast and inexpensive digital signal processors), there is a growing interest in advanced courses in DSP covering a variety of topics. This book was written with the goal of satisfying, in part, the resulting need for textbooks covering these advanced topics.
Most of the material contained in this book was first published in 1992 by the Macmillan Publishing Company, in a book entitled Advanced Digital Signal Processing (which went out of print in 1997). This new book differs from the earlier publication by the inclusion of a new chapter (Chapter 7) on QRD-based fast adaptive filter algorithms, and the deletion of a chapter on multirate signal processing. The other chapters have remained essentially the same.
The major focus of this book is on algorithms for statistical signal processing. Chapter 2 treats computationally efficient algorithms for convolution and for the computation of the discrete Fourier transform. Chapter 3 treats linear prediction and optimum Wiener filters; included in this chapter is a description of the Levinson-Durbin and Schur algorithms. Chapter 4 considers the filter design problem based on the least-squares method and describes several methods for solving least squares problems, including the Givens transformation, the Householder transformation, and singular-value decomposition. Chapter 5 treats single-channel adaptive filters based on the LMS algorithm and on recursive least-squares algorithms. Chapter 6 describes computationally efficient recursive least-squares algorithms for multichannel signals. Chapter 7 is focused on the uses of signal flow graphs for deriving computationally efficient adaptive filter algorithms based on the QR decomposition. Chapter 8 deals with power spectrum estimation, including both parametric and nonparametric methods. Chapter 9 describes the use of higher-order statistical methods for signal modeling and system identification.
Although the material in this book was written by six different authors, we have tried very hard to maintain common notation throughout the book. We believe we have succeeded in developing a coherent treatment of the major topics outlined in the preceding overview. Chapter 1 provides an introduction to selected basic DSP material that is typically found in a first-level DSP text, and also serves to establish some of the notation used throughout the book.
In our treatment of the various topics covered herein, we generally assume that the reader has had a prior course on the fundamentals of digital signal processing. The fundamental topics assumed as background include the z-transform, the analysis and characterization of discrete-time systems, the Fourier transform, the discrete Fourier transform (DFT), and the design of FIR and IIR digital filters.
John G. Proakis
Charles M. Rader Fuyun Ling
Chrysostomos L. Nikias
Marc Moonen
Ian K Proudler
Most helpful customer reviews
11 of 11 people found the following review helpful.
Excellent content, average presentation
By Amazon Customer
The high point of this book is an extensive collection of algorithms and an excellent set of references for further research. Each topic is dealt with in an orderly fashion so that the simple (and usually chronologically earlier) proposals in an area appear first, followed by more complex efficient algorithms.
I have been through the first six (of the total nine) chapters in good detail. The chapters on FFT (1 and 2) and Linear prediction (chapter 3) are well done and serve as an excellent platform to get into the subject. The material is easily implemented in MATLAB using the description in the chapters.
Chapter 4 presents a detailed introduction to least-squares algorithms with a pretty good theoretical treatment. The material presented motivates the merits of least-squares approaches and lays out the various numerical approaches to solving such problems in practice. Chapter 5 and 6 follow up on this introduction to present the specific algorithms for Recursive Least-squares, Lattice-ladder algorithms, stabilized fast RLS etc.
The book gets only 4-stars because of problems with presentation. In the chapters 4,5,6 there is an inconsistency in the symbols used. The symbols used are also not readily related to the quantities they are supposed to represent. Instead of repeating a simple equation, the book often refers to equation numbers in some other part of the chapter or sometimes in other chapters. In some sections algorithms and alternative strategies just appear one after another without a good "big-picture". A flow-chart or some kind of a schematic to help classify the various techniques would enhance the utility of this book manifold (e.g., see "Fundamentals of Statistical Signal Processing" by Steven M. Kay).
Overall, I recommend this book as a very useful starting point for anyone (with a basic DSP background) interested in implementing statistical signal processing algorithms. It is also an excellent survey of existing literature.
0 of 5 people found the following review helpful.
An Excellent book in Advance DSP
By A Customer
This book is a must for any one who wants to read about Advance topics in DSP. Very informative book. It is a book of Proakis.
See all 2 customer reviews...
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