good book | Applied Optimal Estimation

EE grad student (physics & matl eng background) | Applied Optimal Estimation

books:


	• Applied Optimal Estimation MIT Press, 1974 - 374 pages average customer review:based on 11 reviews
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highly recommended

concise and clear

This is a concise record of Kalman filtering and related estimation methods. It briefly covers both discrete time and continuous estimators and is particularly useful to extend your knowledge of if you are already familiar with some of the material. Also, the appendix has an interesting comparison of the analogous terms and concepts for estimation and control theory. The book makes a breadth of topics accessible to students, practicing engineers and others.

good book

this book is not only cheap! but is also a very good reference book. It might not be very reader friendly at the beginning, but if you have a sort of background it is ok.

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EE grad student (physics & matl eng background)

As you can see from the other reviews, Gelb's book is a classic in the Optimal Estimation. I used Gelb to supplement Brown & Hwang's Applied Kalman Filtering text. Brown & Hwang is very readable, written in a conversational tone. It is also *VERY* application focused and has many Matlab examples. However, in their rush to application, I felt Brown & Hwang did not clearly layout the development of the Kalman (it's all there but it is scattered around). On the other hand, Gelb's book is cogent - both clear and concise. I found that Gelb's development and summarization of Kalman and related optimization schemes gave me the foundational understanding to work Brown & Hwang's applicatios.
For this subject, you definitely need probability (thru matrix representation of covariance, means, etc) and stochastic process. Both Gelb and Brown & Hwang review the requisite probability/stochastic processes , but I would recommend a deeper grounding in the subjects (working thru Papoulis, for example).

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Absolutely perfect!

I usually buy books here because they always arrive in time and in prefect conditions, even the used ones! As I live in Brazil, it's really a serious delivery system, because it's not easy to send so far!

great reference and guide to Kalman Filtering

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.

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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 systems

Arthur 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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reviews: page 1, 2, 3

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