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Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches 1st Edition

4.2 out of 5 stars 25 customer reviews
ISBN-13: 860-1422998405
ISBN-10: 0471708585
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Frequently Bought Together

  • Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
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  • Applied Optimal Estimation (MIT Press)
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Editorial Reviews

Review

"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 Reviews.com, 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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Product Details

  • Hardcover: 552 pages
  • Publisher: Wiley-Interscience; 1 edition (June 23, 2006)
  • Language: English
  • ISBN-10: 0471708585
  • ISBN-13: 978-0471708582
  • Product Dimensions: 7.3 x 1.4 x 10.3 inches
  • Shipping Weight: 2.4 pounds (View shipping rates and policies)
  • Average Customer Review: 4.2 out of 5 stars  See all reviews (25 customer reviews)
  • Amazon Best Sellers Rank: #225,803 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

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!
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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.
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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.
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Format: Hardcover Verified Purchase
I've got a fairly extensive background in applications of estimation, but I needed to learn about some of the extensions of the theory, so I bought this primarily for Chapters 7 and 11-12. It's easy to read and follow with lots of the all-important math to fill in the steps. That already gives it a leg up on many of the other books. The price point is excellent. (It's at the low end of the spectrum for hardback text books.) The notation he uses is probably the most common in the field. I'm used to a slightly different notation (from orbital mechanics courses), but it's close enough that I can easily adapt.

For those that are complaining about Appendix C -- don't read it if it bothers you....sheeezzzz.... How narrow minded do you have to be to complain about an author's commentary in an Appendix?? Get over it.

FYI, a really good book that applies some of the estimation techniques to orbital problems is "Modern Orbit Determination: Second Edition" by William Wiesel. It's less than $20 in paperback through Amazon and it walks you through some practical applications. If you are studying orbital mechanics or orbit determination, buy this book and Wiesel's.
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Format: Hardcover Verified Purchase
I have gone through this entire book in detail. I think that it is a great blend of theory and practice based upon the author's teaching experience and practical experience. It is not difficult to read and understand provided you have some background in linear systems. It was very useful for me to catch up with most of the state-of-the art in this field. Since each author reflects his own expertise in his own book, I think it is not necessary to compare this to any other book on State Estimation as it stands out as a classic by itself.
I recommend this book without any reservation and will use it to teach Optimal State Estimation and conduct research in this area.
Dr.Humayun Akhtar
hakhtar0027@gmail.com
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Format: Hardcover Verified Purchase
I am a researcher and my background is in estimation, prediction modeling, and inferential models/methods. I found this book easy to follow (partly because of my background) in writing style.
The book is true to the title and focuses on Kalman filter from several different perspectives (properties, implementation, modifications, etc.)
I am still reading the book, so far I have read ch.3, 5 and 15. I have found a very good comparison of Kalman filter derivation through RLSE route, and Bayesian way. A good comparison of both approaches.
Author is also good in consistently providing the references through out the book, if you choose you can take a deeper dive along the references to sort out details that may be relevant to implementation and research.
I must have to say, a prior applied background is necessary to really appreciate the contents of the book intuitively.
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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.
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