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Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory (v. 1), by Steven M. Kay
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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.
- Sales Rank: #107492 in Books
- Published on: 1993-04-05
- Original language: English
- Number of items: 1
- Dimensions: 9.40" h x 1.00" w x 7.10" l, 2.17 pounds
- Binding: Hardcover
- 625 pages
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
For those involved in the design and implementation of signal processing algorithms, this book strikes a balance between highly theoretical expositions and the more practical treatments, covering only those approaches necessary for obtaining an optimal estimator and analyzing its performance. Author Steven M. Kay discusses classical estimation followed by Bayesian estimation, and illustrates the theory with numerous pedagogical and real-world examples. Special features include over 230 problems designed to reinforce basic concepts and to derive additional results; summary chapter containing an overview of all principal methods and the rationale for choosing a particular one; unified treatment of Wiener and Kalman filtering; estimation approaches for complex data and parameters; and over 100 examples, including real-world applications to high resolution spectral analysis, system identification, digital filter design, adaptive noise cancelation, adaptive beamforming, tracking and localization, and more. Students as well as practicing engineers will find Fundamentals of Statistical Signal Processing an invaluable introduction to parameter estimation theory and a convenient reference for the design of successful parameter estimation algorithms.
Most helpful customer reviews
13 of 14 people found the following review helpful.
Best textbook I've used.
By carbone1@aol.com
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.
6 of 6 people found the following review helpful.
Great Content, questionable Binding
By Hamid
Very well written and easy to follow. The systematic approach to the various forms of estimators clears up what was was originally a 'mess' of estimators and gives concise summaries on the interrelation between them.
My only complaint is the poor quality binding which is already starting to fall apart after less than a three months of normal usage. For this price, a proper binding would have been in order.
5 of 5 people found the following review helpful.
great textbook for class or for self study
By Daniel MMM
Wonderful book. Explanations are clear and examples are plenty and very insightful. The author's prose is somewhat spartan but very accessible and to the point. It is a book for studying and reading with a pen and pencil while working through the exercises as you stride along. One can definitely learn a lot about estimation from it: Cramer-Rao lower bound, minimum variance estimators, linear data model, least squares estimation, maximum likelihood, method of moments, MAP estimation, bayesian estimation and all. Later chapters DO build on top of previous ones, so it is NOT a book to read here and there or to use as a reference, unless you have already worked through it before. End of chapter problems do not come with solutions, but are very cleverly thought out to add more to what has been learned in the chapter. Plenty of examples throughout the book. Lots of back-references to examples in previous chapters and back-references to previous sections. Very much like a textbook, either for class or self study. In other words, the author has what it takes to write a good (text)book. Good no, wonderful. Thumbs up and five stars.
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