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Linear Estimation
 
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Linear Estimation (Paperback)

by Thomas Kailath (Author), Ali H. Sayed (Author), Babak Hassibi (Author)
5.0 out of 5 stars  See all reviews (1 customer review)
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Product details

  • Paperback: 854 pages
  • Publisher: Prentice Hall (19 April 2000)
  • Language English
  • ISBN-10: 0130224642
  • ISBN-13: 978-0130224644
  • Product Dimensions: 23.1 x 17.8 x 4.6 cm
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon.co.uk Sales Rank: 1,161,797 in Books (See Bestsellers in Books)

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Product Description

Product Description

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.



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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5.0 out of 5 stars Linear Estimation, 18 Dec 2002
By A Customer
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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