Algorithms for Statistical Signal Processing

Features

• Cover Type: Hard Cover with 564 pages
• Published by: Prentice Hall
• Edition: 1st Edition January 15, 2002
• Written in: English
• ISBN 10 Number: 0130622192
• ISBN 13 Number: 978-0130622198
• Book Dimensions: 9.5 x 7.2 x 1 inches
• Weighs: 2.2 pounds

Product Description
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.

Back Cover Copy
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.

Reader Reviews
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.