Mathematical Methods and Algorithms for Signal Processing

by Todd K. Moon, Wynn C. Stirling

Publisher: Prentice Hall
ISBN: 0201361868

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Book Description

Mathematical Methods and Algorithms for Signal Processing tackles the challenge of providing readers and practitioners with the broad tools of mathematics employed in modern signal processing. Building from an assumed background in signals and stochastic processes, the book provides a solid foundation in analysis, linear algebra, optimization, and statistical signal processing.

FEATURES/BENEFITS

Many MATLAB algorithms and examples.

• Allow the reader to understand more deeply by seeing the implementation and to learn by doing.

A strong foundation which motivates the development of advanced concepts, removing the "mysteries" frequently encountered by users—Geometric insight is presented wherever possible.

• Readers develop maturity to read literature, and develop confidence in their abilities. Ex. Ch. 2, 3

Solid introduction to wavelets in the context of vector spaces, Including transform algorithms and basic theory.

• Presents this important and modern topic in a context that should help the readers understanding. Ex. Ch. 3

Interesting modern topics not available in many other signal processing texts—Such as the EM algorithm, blind source separation, projection on convex sets, etc., in addition to many more conventional topics such as spectrum estimation, adaptive filtering, etc.

• Motivate reader interest by presenting the field as dynamic, with an enormous number of useful applications.

Review of many signal models, in time domain, frequency domain, and state space domain, showing relationships between them, and issues related to their applications.

• Readers can learn to move among the various forms, and understand how they relate. Also, come to understand the importance of a good signal model in approaching new problems. Ex. Ch. 1

Presents path algorithms (dynamic programming and Viterbi) with many applications.

• Coverage of detection and estimation theory. Learning to employ the tools they have gained in the first part, overcoming some of the algebraic difficulties frequently encountered in this area. Ex. Ch. 10

More than one approach to some problems.

• In QR factorization and the Kalman filter, for example, multiple approaches are presented so the reader can gain insight and approach the realization that there is more than one way to solve the most interesting problems. Ex. Ch. 5, 14

Customer Reviews

My undergraduate and graduate degrees both had a focus in Digital Signal Processing. Looking at my personal library, I have over 25 books that cover material that is touched on in this book. But this book is my absolute go-to. It's clear, comprehensive, and gives great references for further study.

Recently purchased this book and from reading parts of it the book is a very good source of the mathematics associated with signal processing. However the publisher in the latest printing I just received no longer provides the promised CDROM and the listed website where to obtain the MATLAB material on the CDROM is also no longer supported by the publisher. An e-mail response to my inquiry about the missing MATLAB code source from a representative of the publisher indicates that the book is too old and their is no need to provide the material anymore.

This book deals with the actual Mathematical foundations of Signal Processing algorithms that are used in a wide array of applications (like beamforming, communications, voice recognition etc..). It starts with very good introduction of Vector Spaces and Linear Algebra, and follows it with sections on Detection/Estimation Theory, Optimization Methods and Iterative/Recursive Algorithms. In my opinion, the most important feature of the book that makes it impressive is its wide coverage of topics and the different angle (mathematical rigor combined with proper explanations) at which these topics are approached. Note that this book will make more sense to an advanced student in EE rather than a beginner.