Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
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| Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches | |||||||||||||||||||||||||||||
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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(r)-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. A solutions manual is available upon request from the Wiley editorial board. |
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| 11-23-08 | 5 | (NA) |
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This book covers all important parts of state estimation. Starts with statistics preview and covers all common topics in Kalman filter theory including EKF, UKF, H_inf.
The book is written in very good style, all parts are clear to understand. Every topic is supported by examples that usually brings more light into the problems. I strongly recommend this book to everyone who is interested in Kalman filter theory. This book is certainly suitable to those who are new to this field. It has become my most favourite. (Review Data Last Updated: 2008-11-30 11:02:01 EST)
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| 11-12-08 | 4 | (NA) |
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Good introductory book on the subject. It is very easy to understand and does not require much background from the reader. For the more technically advanced readers, the pace may be a bit too slow. But nevertheless, I think it's a good introductory/self-study book especially for engineers.
I would also recommend Gelb's book ` Applied Optimal Estimation `. (Review Data Last Updated: 2008-11-23 09:58:21 EST)
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| 10-14-08 | 2 | 0\1 |
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I got this book for review purposes, since I have been out of the practice for a while. Though the book jumps around a bit, and doesn't build the mathematical foundation that I was used to in graduate school, I would say that it might suffice as a reference but I wouldn't recommend it as the only text to use on the subject. I found the book, "Applied Optimal Estimation" to have the right mix of theory and application (Arthur Gelb). That book also builds itself up pretty well without going off on any tangents like Simon does.
I can only give it two stars, instead of three, for the following reason: The most regrettable portion of this book was Appendix C, where optimal estimation is made analogous to finding the meaning of life (or finding truth about the big philosophical questions). I thought this might be a form of humor - but upon reading the appendix I found that it was actually an opportunity for the author to express his christian beliefs. At the very least, I think he should have left the conversation at his family dinner table where he probably hatched it - but now that pandora is out of the box I would like to speak about it. The funny thing is that he equates the idea of finding an optimal filter to finding the optimal "world view" (referring to answering the big questions with a his optimal belief). Every scientist will tell you that the only way to go about answering such questions, with something meaningfull as to shape our view of the world, is a practice where you can form a hypothesis and TEST it. Something you can't do with the big questions. The religious system you are asserting is an unobservable one Dr Simon!! With regard to the meaning of life, the only reasonable (and optimal) world view is: "I don't know" (and I am certain that Simon doesn't know either). (Review Data Last Updated: 2008-11-13 10:27:40 EST)
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| 08-28-08 | 5 | 0\1 |
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this book is very well written, easy to follow, with a lot of topics, and the derivations are shown thoroghly and in detail. One of the best estimation books I used. This book is recommended for both beginners andadvanced in estimation theory.
(Review Data Last Updated: 2008-10-15 12:59:04 EST)
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| 04-23-08 | 5 | 5\5 |
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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! (Review Data Last Updated: 2008-08-30 09:53:22 EST)
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| 10-11-07 | 5 | 1\1 |
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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). (Review Data Last Updated: 2008-04-24 01:40:18 EST)
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