Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory (v. 1) [Hardcover]

Steven M. Kay (Author)

Book Description

Publication Date: April 5, 1993 | ISBN-10: 0133457117 | ISBN-13: 978-0133457117 | Edition: 1

A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms. Covers important approaches to obtaining an optimal estimator and analyzing its performance; and includes numerous examples as well as applications to real- world problems. MARKETS: For practicing engineers and scientists who design and analyze signal processing systems, i.e., to extract information from noisy signals — radar engineer, sonar engineer, geophysicist, oceanographer, biomedical engineer, communications engineer, economist, statistician, physicist, etc.

Editorial Reviews

Amazon.com Review

This text is geared towards a one-semester graduate-level course in statistical signal processing and estimation theory. The author balances technical detail with practical and implementation issues, delivering an exposition that is both theoretically rigorous and application-oriented. The book covers topics such as minimum variance unbiased estimators, the Cramer-Rao bound, best linear unbiased estimators, maximum likelihood estimation, recursive least squares, Bayesian estimation techniques, and the Wiener and Kalman filters. The author provides numerous examples, which illustrate both theory and applications for problems such as high-resolution spectral analysis, system identification, digital filter design, adaptive beamforming and noise cancellation, and tracking and localization. The primary audience will be those involved in the design and implementation of optimal estimation algorithms on digital computers. The text assumes that you have a background in probability and random processes and linear and matrix algebra and exposure to basic signal processing. Students as well as researchers and practicing engineers will find the text an invaluable introduction and resource for scalar and vector parameter estimation theory and a convenient reference for the design of successive parameter estimation algorithms.

From the Back Cover

A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms. Covers important approaches to obtaining an optimal estimator and analyzing its performance; and includes numerous examples as well as applications to real- world problems. MARKETS: For practicing engineers and scientists who design and analyze signal processing systems, i.e., to extract information from noisy signals — radar engineer, sonar engineer, geophysicist, oceanographer, biomedical engineer, communications engineer, economist, statistician, physicist, etc.

Product Details

• Hardcover: 625 pages
• Publisher: Prentice Hall; 1 edition (April 5, 1993)
• Language: English
• ISBN-10: 0133457117
• ISBN-13: 978-0133457117
• Product Dimensions: 9.5 x 7.2 x 1.1 inches
• Shipping Weight: 2.3 pounds (View shipping rates and policies)
• Average Customer Review: 4.8 out of 5 stars  See all reviews (14 customer reviews)
• Amazon Best Sellers Rank: #275,232 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

18 of 18 people found the following review helpful:

5.0 out of 5 stars A Landmark Book on Estimation Theory, August 9, 2000

By 

Peter J. Kootsookos (West Hartford, CT, USA) - See all my reviews
(REAL NAME)   

This review is from: Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory (v. 1) (Hardcover)

In this book, Steven M. Kay has produced an excellent tutorial and research reference book on estimation theory. The book covers enough introductory material for someone with a reasonable undergraduate understanding of statistics to pick up the ideas quickly. The theory is illustrated with very concrete examples; the examples give an "under-the-hood" insight into the solution of some common estimation problems in signal processing. If you're a statistician, you might not like this book. If you're an engineer, you will like it.

13 of 13 people found the following review helpful:

5.0 out of 5 stars Legendary and masterpiece in estimation theory, June 12, 2004

By 

Navid Lashkarian (Pleasanton, CA United States) - See all my reviews
(REAL NAME)   

This review is from: Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory (v. 1) (Hardcover)

Without any hesitation, I consider this book as a masterpiece in the area of statistical signal processing. Kay takes the reader to the journey of estimation theory as if a science teacher takes his students to a field trip. The one special feature of this book is the convergence of thought that reader obtains upon reading the book. Kay lays a fundamental bridge between various estimators using his succinct style for describing the subject.

Few special areas require more attention in this book. For example the coverage of EM methods is very condense and requires more elaboration. Also there is no discussion on the estimation methods using higher order statistics.

Overall I consider this book as the best book I have read ever and I highly recommend this book to those who want to obtain an ever-lasting view on statistical signal processing.

9 of 9 people found the following review helpful:

5.0 out of 5 stars couldn't rate 6... a must !, August 11, 2003

By A Customer

This review is from: Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory (v. 1) (Hardcover)

I've had tough courses on statistical signal processing as a post-grade student. I am often confused in front of a problem and turning back to the notes taken in class doesn't help much.
When you read this book all gets bright. I am still wondering how some teachers can be so confusing while such good books do exist...
However don't count on it for in depth mathematical demonstrations, it starts with a practical problem and explains how to model things. Thus it is a bit bottom-up but anyway starting from a good graduate level in signal and stats.
I got this one at the library but already ordered a copy for myself and am planning to get part2 on detection.