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Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches Hardcover – Jun 23 2006

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Product Details

  • Hardcover: 552 pages
  • Publisher: Wiley-Interscience; 1 edition (June 23 2006)
  • Language: English
  • ISBN-10: 0471708585
  • ISBN-13: 978-0471708582
  • Product Dimensions: 18.5 x 3.6 x 26.2 cm
  • Shipping Weight: 1.1 Kg
  • Average Customer Review: Be the first to review this item
  • Amazon Bestsellers Rank: #368,493 in Books (See Top 100 in Books)
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Product Description


"This book is obviously written with care and reads very easily. A very valuable resource for students, teachers, and practitioners…highly recommended." (CHOICE, February 2007)

"The dozens of helpful step-by-step examples, visual illustrations, and lists of exercises proposed at the end of each chapter significantly facilitate a reader's understanding of the book's content." (Computing, December 4, 2006)

From the Back Cover

A bottom-up approach that enables readers to master and apply the latest techniques in state estimation

This book offers the best mathematical approaches to estimating the state of a general system. The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state estimation techniques confidently across a variety of fields in science and engineering.

While there are other textbooks that treat state estimation, this one offers special features and a unique perspective and pedagogical approach that speed learning:

  • Straightforward, bottom-up approach begins with basic concepts and then builds step by step to more advanced topics for a clear understanding of state estimation
  • Simple examples and problems that require only paper and pen to solve lead to an intuitive understanding of how theory works in practice
  • MATLAB®-based source code that corresponds to examples in the book, available on the author's Web site, enables readers to recreate results and experiment with other simulation setups and parameters

Armed with a solid foundation in the basics, readers are presented with a careful treatment of advanced topics, including unscented filtering, high order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman/H? filtering.

Problems at the end of each chapter include both written exercises and computer exercises. Written exercises focus on improving the reader's understanding of theory and key concepts, whereas computer exercises help readers apply theory to problems similar to ones they are likely to encounter in industry. A solutions manual is available for instructors.

With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory. It also serves as a reference for engineers and science professionals across a wide array of industries.

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Inside This Book

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This chapter reviews some essentials of linear systems theory. Read the first page
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Most Helpful Customer Reviews on (beta) HASH(0xa5a939c0) out of 5 stars 26 reviews
24 of 26 people found the following review helpful
HASH(0xa5bc448c) out of 5 stars Best book for self-study (Optimal Estimation AND Kalman Filtering) Oct. 10 2007
By F. Z. - Published on
Format: Hardcover
I agree with a previous reviewer in that out of all the books I have come across on Optimal Estimation, this is by far the most suitable for self-study. I have found his explanations to be concise and straight-forward. That is, he goes straight to the point and delivers the concepts using simple/common language which is non-characteristic for academic books in the areas of Systems and Control. For a sample text on the subject written by him check his article on Kalman Filtering on the site embedded[dot]com

While conducting research as part of an Independent Study course, I have treasured this book like no other since it continuously serves as a valuable reference. The first two chapters which review the underlying mathematics (linear algebra and probability) necessary for understanding the central themes of the book are also above the usual presentation in related books. Needless to say that readers should not expect to learn the Math from this book alone, however, they can expect to find in these chapters most of the topics that usually need a quick review to make sense of higher-level concepts in the text.

I cannot stress enough that his use of language and clear explanations make this an easy-to-read textbook which simplifies the understanding of the topics. Do not get me wrong though, to really understand the problem of state estimation the readers need to be quite prepared in different areas of Engineering and Mathematics (hence my motivation for self-study).
9 of 10 people found the following review helpful
HASH(0xa5bc44e0) out of 5 stars Very very good April 23 2008
By JDR - Published on
Format: Hardcover
A very clear, well written book that takes you step by step from the algebra and statistics basics to the most advanced developments of dynamic systems. The first part of the book is about providing all the knowledge required for the rest of the book in linear system theory (1st chapter), probability theory (2nd chapter) and least square estimation (3rd chapter). These chapters are very clear and, in my opinion, easy to follow for the non specialist. The second part is about the core subject, Kalman filter. Again, it is very clear and the fact that it very consistent with the 1st part in term of notation makes it very readable. Subsequent parts are more advanced topics but again nicely elaborate on the previous chapters and hence very easy to understand. I'll repeat myself but that really what I enjoyed most with this book: it is very progressive and takes you step by step.
I even think this is the best technical book I have ever read. Dynamic systems made easy!
12 of 14 people found the following review helpful
HASH(0xa5bc4918) out of 5 stars The best book on Kalman filters Aug. 13 2007
By Bob Forex - Published on
Format: Hardcover
I have 4 books on Optimal state estimation:
_ Applied Optimal Estimation of Arthur Gelb.
_ Optimal Control and Estimation by Robert F. Stengel
_ Optimal Control and Estimation Theory by George M. Siouris
_ Optimal State Estimation By Dan Simon

Of the 4, Dan Simon's Optimal State Estimation is by far the most useful for a GNC Engineer like me. He strikes a good balance between theory and practice and his examples are really useful. I find his treatment of EKF excellent.
5 of 5 people found the following review helpful
HASH(0xa5bc4ce4) out of 5 stars Excellent for a newcomer Feb. 4 2009
By T. Driver - Published on
Format: Hardcover Verified Purchase
This book relates control theory elegantly, to those with a scientific background, but not much control theory history. Dan uses well laid out algorithmic approaches, suitable for programming, and examples to explain the details and show the complexities in action. I especially like the non-linear filtering chapters, and the comparison s between the Kalman Filter and other approaches (Particle Filter, etc.) I have several estimation/control theory texts, and this is the one I carry around with me.
3 of 3 people found the following review helpful
HASH(0xa5bc4c90) out of 5 stars Good over all book with plenty of proofs March 9 2009
By P. Abeles - Published on
Format: Hardcover Verified Purchase
Overall I found the book to be very well written with plenty of proofs, which I really like. In the sections that I looked at (with one exception) I did not find any errors. For the more recent material, towards the end, I would highly recommend looking at the original papers in addition to what's in the book. Out of necessity some things are glossed over. In the case of the unscented Kalman filter the algorithm presented in the book has some issues. It is not exactly the same algorithm as in the original papers and will in fact produce a covariance matrix which is incorrect. However, on the whole I would highly recommend the book, especially for self learners.