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Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory (v. 1) |  | Author: Steven M. Kay
Publisher: Prentice Hall
Category: Book
List Price: $132.00
Buy New: $70.00
as of 7/29/2010 17:45 CDT details
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Seller: bko1939
Rating:  10 reviews
Media: Hardcover
Edition: 1
Pages: 625
Number Of Items: 1
Shipping Weight (lbs): 2.3
Dimensions (in): 9.2 x 7.4 x 1.3
ISBN: 0133457117
Dewey Decimal Number: 621.3822
UPC: 076092031871
EAN: 9780133457117
Availability: Usually ships in 1-2 business days
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| 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.
Product Description
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.
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| Customer Reviews:
Showing reviews 1-5 of 10
 A Landmark Book on Estimation Theory August 9, 2000
Peter J. Kootsookos (West Hartford, CT, USA)
15 out of 15 found this review helpful
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.
Legendary and masterpiece in estimation theory June 12, 2004
Navid Lashkarian (Pleasanton, CA United States)
9 out of 9 found this review helpful
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.
couldn't rate 6... a must ! August 11, 2003
8 out of 8 found this review helpful
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.
Best textbook I've used. June 11, 1999
carbone1@aol.com (Cranston,RI)
8 out of 9 found this review helpful
The theory is explaned well and motivated, but what makes the book great are the examples. There are many worked examples and they are chosen to make things very clear.
An Excellent Book in Estimation Theory March 4, 2002
4 out of 4 found this review helpful
This book was asigned to me for a graduate course in Statistical Signal Estimation. The book was very useful and easy to read. It was well written and had helpful examples. I recommend this book for any one who wants to learn about Estimation Theory.
Showing reviews 1-5 of 10
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