Free Ebook Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory (v. 1)
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Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory (v. 1)
Free Ebook Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory (v. 1)
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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.
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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.
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Product details
Hardcover: 608 pages
Publisher: Prentice Hall; 1 edition (April 5, 1993)
Language: English
ISBN-10: 0133457117
ISBN-13: 978-0133457117
Product Dimensions:
7.4 x 1.4 x 9.2 inches
Shipping Weight: 2.3 pounds (View shipping rates and policies)
Average Customer Review:
4.3 out of 5 stars
20 customer reviews
Amazon Best Sellers Rank:
#60,930 in Books (See Top 100 in Books)
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.
This is an excellent book for someone who needs to pick up the essentials of estimation theory in a hurry. I have never taken a course on statistical signal processing or information theory, and yet I was able to learn the subject just from reading this textbook to the point where I can do graduate level research in the area. Kay makes the text very readable so one can just follow along as if attending lectures, and he does a brilliant job of striking the right balance of theory and real-world examples so you can really understand the material. I did not have enough time to try many of the exercise problems, but the ones that I did try were excellent. I was however able to gain enough understanding through the text and examples to actually put it into practice in my own research, so that is definitely the sign of a great textbook.
I bought this book because I wanted an practical adjunct to accompany my study of linear modeling theory using Stapleton's "Linear Statistical Models." Stapleton's text is theoretical where results are explained. I had hoped Kay's book would provide some practical examples from engineering and technology. The problem was that Kay's book just states results. There is no development of the results from beginning theory with examples. Why should one make the assumptions he makes?But this is just my take. From perusing the other reviews of Kay's book, he produces what some other reader's want.
This book deserves this rate because it is simply the most didactical book about Estimation Theory that I have read.I liked everything, each chapter. The book is perfect for a graduate one-semester course on Estimation Theory and for every one who needs Estimation Theory.
this book is in my opinion one of the best books about this topic. It has a very neat and clear language.
Very concise and fluent and easy to follow. Love it!!!!I'm studying it on my own and I could easily follow this book.
This book is a masterpiece ! Easy to read and to follow. I've bought many book in Signal Processing (Estimation Theory, Spectral Analysis, Time Frequency, Pattern recognition)and, for me, this one is the best !
Excellent!
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