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Statistical Digital Signal Processing and Modeling

Statistical Digital Signal Processing and Modeling

Monson H. Hayes

ISBN: 978-0-471-59431-4

Apr 1996

624 pages

Select type: Paperback

In Stock



This new text responds to the dramatic growth in digital signal processing (DSP) over the past decade, and is the product of many years of teaching an advanced DSP course at Georgia Tech. While the focal point of the text is signal modeling, it integrates and explores the relationships of signal modeling to the important problems of optimal filtering, spectrum estimation, and adaptive filtering.

Coverage is equally divided between the theory and philosophy of statistical signal processing, and the algorithms that are used to solve related problems. The text reflects the author's philosophy that a deep understanding of signal processing is accomplished best through working problems. For this reason, the book is loaded with worked examples, homework problems, and MATLAB computer exercises. While the examples serve to illustrate the ideas developed in the book, the problems seek to motivate and challenge the student and the computer exercises allow the student to experiment with signal processing algorithms on complex signals.

Professor Hayes is recognized as a leader in the signal processing community, particularly for his work in signal reconstruction and image processing. This text is suitable for senior/graduate level courses in advanced DSP or digital filtering found in Electrical Engineering Departments. Prerequisites include basic courses in DSP and probability theory.

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Discrete-Time Random Processes.

Signal Modeling.

The Levinson Recursion.

Lattice Filters.

Wiener Filtering.

Spectrum Estimation.

Adaptive Filtering.


Table of Symbols.

  • Provides complete coverage of signal modeling, optimum filtering, spectrum estimation, and adaptive filtering: four inter-related and essential topics in digital signal processing applications.
  • Includes computer exercises using MATLAB to give students experience with real world applications and to assist instructors who require a laboratory component.
  • Many worked examples illustrate particular algorithms and techniques in use in practical settings.
  • Large number of homework problems enhance student understanding and set the stage for topics to be presented later in the text.
  • Highlighted key equations and results, table summaries of each algorithm, list of key terms, chapter summaries, motivating chapter introductions, and a review of basic DSP and linear algebra (Chapter 2) serve as helpful study aids for students.