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Linear Estimation Paperback – Mar 31 2000


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Product Details

  • Paperback: 854 pages
  • Publisher: Prentice Hall; 1 edition (March 31 2000)
  • Language: English
  • ISBN-10: 0130224642
  • ISBN-13: 978-0130224644
  • Product Dimensions: 4.8 x 17.5 x 22.8 cm
  • Shipping Weight: 1.3 Kg
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (3 customer reviews)
  • Amazon Bestsellers Rank: #1,820,989 in Books (See Top 100 in Books)
  • See Complete Table of Contents

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Format: Paperback
I came to this book with a need to become familiar with Kalman filters. I've read the first two chapters so far with great pleasure. Professor Kailath develops the material beautifully; his profound mastery of the field is evident in every paragraph. The material is concentrated, but is presented in a highly readable compelling style. The reader is expected to be comfortable with the basics of linear systems theory, probability, and matrix analysis, although extensive appendices provide the necessary background.
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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.
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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.
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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 6 reviews
15 of 16 people found the following review helpful
Linear Estimation from A to Z. Feb. 5 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.
11 of 12 people found the following review helpful
Wonderful and insightful Sept. 17 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
Excellent text Sept. 25 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.
4 of 5 people found the following review helpful
Well-organized, readable, beautiful Oct. 9 2002
By Jay - Published on Amazon.com
Format: Paperback
I came to this book with a need to become familiar with Kalman filters. I've read the first two chapters so far with great pleasure. Professor Kailath develops the material beautifully; his profound mastery of the field is evident in every paragraph. The material is concentrated, but is presented in a highly readable compelling style. The reader is expected to be comfortable with the basics of linear systems theory, probability, and matrix analysis, although extensive appendices provide the necessary background.
very poorly printed book. Nov. 12 2013
By just_me - Published on Amazon.com
Format: Paperback Verified Purchase
My review is not about the content of the book but its printing.
The printing is just a photocopy and the binding is very poor.
I think the book will split into many pieces just within a few days.
The book is very expensive but then how can the printing be so poor?
I am totally shocked!
Never ever buy this book! This is my suggestion. Just take a photocopy from library and you will
have the same book and will profit at least 20 times!

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