Fundamentals of Statistical Processing, Volume I: Estimation Theory (Prentice Hall Signal Processing Series) | 
| Author: Steven M. Kay
Publisher: Prentice Hall PTR
Category: Book
List Price: $115.00
Buy New: $74.64
You Save: $40.36 (35%)
New (20) Used (12) from $65.00
Avg. Customer Rating:  9 reviews
Sales Rank: 54207
Media: Hardcover
Edition: 1
Number Of Items: 1
Pages: 625
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
ASIN: 0133457117
Publication Date: April 5, 1993
Availability: Usually ships in 1-2 business days
Shipping: Expedited shipping available
Condition: New Book, Hardcover. Same Edition As Amazon's Description! Never Been Read! Buy Now!
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| Editorial Reviews:
Amazon.com
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: Read 4 more reviews...
A Must HAVE!!!!!! April 5, 2008
Excellent topic coverage by what I consider to be the best mind in the field today!
Perfect December 4, 2007
1 out of 1 found this review helpful
One of the best written textbooks I have ever read, in any field. Crystal clear, and is a gold mine of knowledge.
Legendary and masterpiece in estimation theory June 13, 2004
8 out of 8 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 12, 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.
A reference for self-directed study June 9, 2003
4 out of 4 found this review helpful
This text is very good for those who start doing research in statistical signal processing. A lot of explanations, technical terms are well presented and consistent, plus a number of examples that help you to learn about different statistical signal processing concepts and algorithms. Research students can be beneficial alot from this text.
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