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This is the first book on the optimal estimation that places its major emphasis on practical applications, treating the subject more from an engineering than a mathematical orientation. Even so, theoretical and mathematical concepts are introduced and developed sufficiently to make the book a self-contained source of instruction for readers without prior knowledge of the basic principles of the field. The work is the product of the technical staff of the The Analytic Sciences Corporation (TASC), an organization whose success has resulted largely from its applications of optimal estimation techniques to a wide variety of real situations involving large-scale systemsArthur Gelb writes in the Foreword that "It is our intent throughout to provide a simple and interesting picture of the central issues underlying modern estimation theory and practice. Heuristic, rather than theoretically elegant, arguments are used extensively, with emphasis on physical insights and key questions of practical importance."Numerous illustrative examples, many based on actual applications, have been interspersed throughout the text to lead the student to a concrete understanding of the theoretical material. The inclusion of problems with "built-in" answers at the end of each of the nine chapters further enhances the self-study potential of the text.After a brief historical prelude, the book introduces the mathematics underlying random process theory and state-space characterization of linear dynamic systems. The theory and practice of optimal estimation is them presented, including filtering, smoothing, and prediction. Both linear and non-linear systems, and continuous- and discrete-time cases, are covered in considerable detail. New results are described concerning the application of covariance analysis to non-linear systems and the connection between observers and optimal estimators. The final chapters treat such practical and often pivotal issues as suboptimal structure, and computer loading considerations.This book is an outgrowth of a course given by TASC at a number of US Government facilities. Virtually all of the members of the TASC technical staff have, at one time and in one way or another, contributed to the material contained in the work
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Einleitungssatz
The development of data processing methods for dealing with random variables can be traced to Gauss (circa 1800), who invented the technique of deterministic least-squares and employed it in a relatively simple orbit measurement problem (Ref. 1). Lesen Sie die erste Seite
Buchdeckel | Copyright | Inhaltsverzeichnis | Auszug | Stichwortverzeichnis | Rückseite
The development of data processing methods for dealing with random variables can be traced to Gauss (circa 1800), who invented the technique of deterministic least-squares and employed it in a relatively simple orbit measurement problem (Ref. 1). Lesen Sie die erste Seite
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2 von 2 Kunden fanden die folgende Rezension hilfreich
Format:Taschenbuch
I received the first copy of the book from my employers when I took a short-course in Kalman Filters to augment my systems knowledge for performing inertial navigation analysis work for missile engineering at TRW in Redondo Beach, CA. That was 20 years or so ago. I can say I have yet to see another book that can match or surpass this one in simplicity in explanations, background materials (linear algebra, linear differential equations, state variable theory - control systems aspect, and random processes), cover many varied areas of applications and nonlinear problems, present the practical discrete-time equations alongside the more theoretically based continuous-time equations and demonstrate their uses and meaning, and discuss practical implemetation issues, schematically depict difficult ideas, equations and concepts through well organized and coherent diagrams and tables, and design manageable and solvable problems as exercises for learning at the end of each chapter, all of these done in a small inexpensive paperback. The book, unlike most others I have read or browsed, does not in any way assume a priori knowledge or basic understanding of Kalman Filters or what they are all about, and presents enough fundamental materials written clearly and lucidly so that any motivated student or worker new to the field can pick up almost everything he or she needs for learning. Though the book is not quite not a theoretical landmark for the mathematically inclined (and makes no bones about it), yet it has enough derivations to make it rigorous in its presentation of the mathematics underlying Kalman Filtering. Perhaps the computer oriented students and professionals will be disappointed because the book predates Matlab, but it did well in the days when analytical software was often custom designed, tailored and developed for engineering and scientific applications. A brief summary - very readable and approachable, unpretentious writing style, a great learning guide for the uninitiated as well as the systems engineering practitioner.
Format:Taschenbuch
This is a textbook covering optimal estimation, solutions to stochastic differential equations with clear discussions on implementation. This text is a must have for solving multi-dimensional parameter estimation problems.
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16 Rezensionen
58 von 59 Kunden fanden die folgende Rezension hilfreich
Still the Best Introduction to Kalman Filters
22. März 2000
Format:Taschenbuch
I received the first copy of the book from my employers when I took a short-course in Kalman Filters to augment my systems knowledge for performing inertial navigation analysis work for missile engineering at TRW in Redondo Beach, CA. That was 20 years or so ago. I can say I have yet to see another book that can match or surpass this one in simplicity in explanations, background materials (linear algebra, linear differential equations, state variable theory - control systems aspect, and random processes), cover many varied areas of applications and nonlinear problems, present the practical discrete-time equations alongside the more theoretically based continuous-time equations and demonstrate their uses and meaning, and discuss practical implemetation issues, schematically depict difficult ideas, equations and concepts through well organized and coherent diagrams and tables, and design manageable and solvable problems as exercises for learning at the end of each chapter, all of these done in a small inexpensive paperback. The book, unlike most others I have read or browsed, does not in any way assume a priori knowledge or basic understanding of Kalman Filters or what they are all about, and presents enough fundamental materials written clearly and lucidly so that any motivated student or worker new to the field can pick up almost everything he or she needs for learning. Though the book is not quite not a theoretical landmark for the mathematically inclined (and makes no bones about it), yet it has enough derivations to make it rigorous in its presentation of the mathematics underlying Kalman Filtering. Perhaps the computer oriented students and professionals will be disappointed because the book predates Matlab, but it did well in the days when analytical software was often custom designed, tailored and developed for engineering and scientific applications. A brief summary - very readable and approachable, unpretentious writing style, a great learning guide for the uninitiated as well as the systems engineering practitioner.
26 von 26 Kunden fanden die folgende Rezension hilfreich
great reference and guide to Kalman Filtering
22. Februar 2008
Format:Taschenbuch
I worked in the aerospace industry from 1980-1991. During my years at the Aerospace Corporation I got this book as a reference to the application of Kalman filtering to orbit determination and estimation problems. So my experience and appreciation for this book is very similar to my colleagues working nearby me at TRW or Hughes Aircraft and cosequently I am in strong agreement with some of the other amazon reviews of Gelb's book. I always found it to be a key reference for me.
16 von 17 Kunden fanden die folgende Rezension hilfreich
old reliable
22. Juli 2003
Format:Taschenbuch
I first became acquainted with Kalman filters in 1979 when I was required to write software to track targets on a Navy weapons testing range. We used passive hydrophones together with active pingers on the targets, with the ping sequence identifying each target. We received arrival times for each ping, and had to sort them out and solve the intersecting hyperbolic surfaces of rotation to determine the target's position. Since pings had a significant time interval seperating them, we used Kalman filters to interpolate and extrapolate the target's position. I read Kalman's original 1960 paper on the subject, and several other papers, but I knew of no textbook coverage of the subject.
In the mid-1980's I was in graduate school. There I became aware of Gelb's wonderful book. I wished I had had it 6 years earlier!
A neighbor is an econometric quantitative analyst, and he uses Kalman filters for managing hedge funds.
Now I am involved in modelling glucose and insulin levels in diabetics and Kalman filters look like the technique of choice once again. Out with Gelb's book for a quick review. It seems to be timeless in its value because of the excellent treatment of the subject.
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