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Linear Estimation [Paperback]

Thomas Kailath (Author), Ali H. Sayed (Author), Babak Hassibi (Author)

3.0 out of 5 stars See all reviews (2 customer reviews)

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Book Description

Publication Date: 31 Mar 2000 | ISBN-10: 0130224642 | ISBN-13: 978-0130224644 | Edition: 1

This textbook is intended for a graduate-level course and assumes familiarity with basic concepts from matrix theory, linear algebra, and linear system theory. Six appendices at the end of the book provide the reader with enough background and review material in all these areas.

This original work offers the most comprehensive and up-to-date treatment of the important subject of optimal linear estimation, which is encountered in many areas of engineering such as communications, control, and signal processing, and also in several other fields, e.g., econometrics and statistics. The book not only highlights the most significant contributions to this field during the 20th century, including the works of Wiener and Kalman, but it does so in an original and novel manner that paves the way for further developments in the new millennium. This book contains a large collection of problems that complement the text and are an important part of it, in addition to numerous sections that offer interesting historical accounts and insights. The book also includes several results that appear in print for the first time.

#outer_postBodyPS { display: none; } #psGradient { display: none; } #psPlaceHolder { display: none; } #psExpand { display: none; } This textbook is intended for a graduate-level course and assumes familiarity with basic concepts from matrix theory, linear algebra, and linear system theory. Six appendices at the end of the book provide the reader with enough background and review material in all these areas.

Product details

Paperback: 854 pages
Publisher: Prentice Hall; 1 edition (31 Mar 2000)
Language: English
ISBN-10: 0130224642
ISBN-13: 978-0130224644
Product Dimensions: 18.5 x 3.6 x 24.1 cm

Average Customer Review: 3.0 out of 5 stars See all reviews (2 customer reviews)

Amazon Bestsellers Rank: 911,026 in Books (See Top 100 in Books)

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From the Back Cover

This original work offers the most comprehensive and up-to-date treatment of the important subject of optimal linear estimation, which is encountered in many areas of engineering such as communications, control, and signal processing, and also in several other fields, e.g., econometrics and statistics. The book not only highlights the most significant contributions to this field during the 20th century, including the works of Wiener and Kalman, but it does so in an original and novel manner that paves the way for further developments. This book contains a large collection of problems that complement it and are an important part of piece, in addition to numerous sections that offer interesting historical accounts and insights. The book also includes several results that appear in print for the first time.

FEATURES/BENEFITS

Takes a geometric point of view.
Emphasis on the numerically favored array forms of many algorithms.
Emphasis on equivalence and duality concepts for the solution of several related problems in adaptive filtering, estimation, and control.
- These features are generally absent in most prior treatments, ostensibly on the grounds that they are too abstract and complicated. It is the authors' hope that these misconceptions will be dispelled by the presentation herein, and that the fundamental simplicity and power of these ideas will be more widely recognized and exploited. Among other things, these features already yielded new insights and new results for linear and nonlinear problems in areas such as adaptive filtering, quadratic control, and estimation, including the recent Hà theories.

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1.0 out of 5 stars bad paper and bad binding 12 Oct 2012

By Marco

Format:Paperback|Amazon Verified Purchase

The book content is good but this edition, however, as bad paper and even worse binding. bla bli blo blu ble

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5.0 out of 5 stars Linear Estimation 18 Dec 2002

By A Customer

Format:Paperback

This text primarily focuses on estimation problems for finite-dimensional linear systems with state-space models, covering most aspects of an area now generally known as Wiener and Kalman filtering theory. Distinctive features the treatment are the pervasive use of a geometric point of view; the emphasis on the numerically favored square root/array forms of many algorithms; and the emphasis on equivalence and duality concepts for the solution of several related problems in adaptive filtering, estimation, and control. This text reasonably puts emphasis on equivalence and duality concepts for the solution of several related problems in adaptive filtering, estimation, and control. This book convey all these complicated looking formulations in a lucid manner. That is my main reason for recommending this book.

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Amazon.com: 5.0 out of 5 stars 4 reviews

13 of 14 people found the following review helpful

5.0 out of 5 stars Linear Estimation from A to Z. 6 Feb 2001

By Jeffrey Andrews - Published on Amazon.com

Format:Paperback

Kailath, Sayed, and Hassibi do an excellent job of explaining what is a fairly complicated subject. This book is best-suited for scholars who desire a deep understanding of estimation theory. Engineers who want to quickly understand how to implement a Kalman Filter might be better off buying Adaptive Filter Theory by Simon Haykin.

The first chapter provides a good overview of the book, although it makes the most sense once the subject matter of the rest of the book has been digested a bit. A consistent framework emphasizing innovations (or the new information which appears at any iteration) is used throughout the book, and both continuous and discrete-time techniques for stochastic estimation are given nearly equal treatment, although the real-world engineer is likely to be interested in the latter.

Professor Kailath's articulate nature and knack for the clever anecdote or one-liner shines throughout the book, making it, while very mathematical in nature, quite readable for the motivated student.

10 of 11 people found the following review helpful

5.0 out of 5 stars Wonderful and insightful 17 Sep 2001

By Nicolas Chapados - Published on Amazon.com

Format:Paperback

This is one of the best engineering textbooks I have read, period. Although the subject matter is not for the faint-hearted, the authors' attention to pedagogical details shine throughout (repetition is the key to learning). The Kalman filter is introduced naturally as a consequence of a general framework for obtaining the best linear estimator of a random variable given others (earlier observations), and the geometric intuition is stressed repeatedly.

No important issue is omitted, including a very complete treatment of numerical issues and fast algorithms. My only gripe is with the assumption that all model parameters are KNOWN; in other words, the important aspect system identification (parameter estimation, learning, or whatever you call it in your field) is left to other textbooks.

Moreover, and no minor accomplishment, is the amazingly small number of typographical errors (at least up to where I have read so far; a bit over half the book), which is remarkable given the dense mathematical contents.

All in all, I would give it 6 stars if possible. Everything is there: it transmits a deep intuition for the matter, a places it in its historical context through interesting and amusing notes; it leaves the reader fulfilled but not overwhelmed.

3 of 3 people found the following review helpful

5.0 out of 5 stars Excellent text 25 Sep 2005

By Elvis Dieguez - Published on Amazon.com

Format:Paperback

This is an excellent text that covers estimation theory from a modern point of view. It will be especially interesting to anyone with a graduate degree in physics because Kailath, et al derive the theory of linear estimation from a point of view very similar to that of modern quantum mechanics - they even use similar bra/ket notation!

Basic and advanced statistical mathematics is somewhat an implied prerequisite for understanding this text. From what I have seen, I honestly find nothing negative to critique - its probably one of the best technical textbooks I have in my large library.

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