STATISTICAL DIGITAL. SIGNAL PROCESSING. AND MODELING. MONSON H. HAYES. Georgia Institute of Technology. JOHN WILEY & SONS, INC. [Monson H. Hayes] Statistical Digital Signal Proce(terney.info). Pages · Statistical Digital Signal Processing and Modeling. Pages·· Monson H. Hayes-statistical Digital Signal Processing and Modeling-John Wiley & Sons ().pdf - Ebook download as PDF File .pdf) or read book online.

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This book is the culmination of a project that began as a set of notes for a graduate level course that is offered at Georgia Tech. In writing this book, there have been many challenges. One of these was the selection of an appropriate title for the book. Although the title that was selected is Statistical Signal Processing and Modeling, any one of a number of other titles could equally well have been chosen. For example, if the title of a book is to capture its central theme, then the title perhaps could have been Least Squares Theory in Signal Processing. If, on the other hand, the title should reflect the role of the book within the context of a course curriculum, then the title should have been A Second Course in Discrete-Time Signal Processing.

Although the title that was selected is Statistical Signal Processing and Modeling, any one of a number of other titles could equally well have been chosen. For example, if the title of a book is to capture its central theme, then the title perhaps could have been Least Squares Theory in Signal Processing.

If, on the other hand, the title should reflect the role of the book within the context of a course curriculum, then the title should have been A Second Course in Discrete-Time Signal Processing. Whatever the title, the goal of this book remains the same: to provide a comprehensive treatment of signal processing algorithms for modeling discrete-time signals, designing optimum digital filters, estimating the power spectrum of a random process, and designing and implementing adaptive filters.

In looking through the Table of Contents, the reader may wonder what the reasons were in choosing the collection of topics in this book. There are two.

The first is that each topic that has been selected is not only important, in its own right, but is also important in a wide variety of applications such as speech and audio signal processing, image processing, array processing, and digital communications. The second is that, as the reader will soon discover, there is a remarkable relationship that exists between these topics that tie together a number of seemingly unrelated problems and applications.

For example, in Chapter 4 we consider the problem of modeling a signal as the unit sample response of an all-pole filter.

Then, in Chapter 7, we find that all-pole signal modeling is equivalent to the problem of designing an optimum Wiener filter for linear prediction. Since both problems require finding the solution to a set of Toeplitz linear equations, the Levinson recursion that is derived in Chapter 5 may be used to solve both problems, and the properties that are shown to apply to one problem may be applied to the other.

Later, in Chapter 8, we find that an all-pole model performs a maximum entropy extrapolation of a partial autocorrelation sequence and leads, therefore, to the maximum entropy method of spectrum estimation.

This book possesses some unique features that set it apart from other treatments of statistical signal processing and modeling. First, each chapter contains numerous examples that illustrate the algorithms and techniques presented in the text.

Harry Potter. Popular Features. New in Statistical Digital Signal Processing and Modeling. Description 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. Table of contents Background.

Discrete--Time Random Processes. Signal Modeling. The Levinson Recursion.

Lattice Filters. Wiener Filtering. Spectrum Estimation. Adaptive Filtering.