Kalman Filter for Beginners: with MATLAB Examples

26 customer reviews
ISBN-13: 978-1463648350
ISBN-10: 1463648359
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Kalman Filter for Beginners: with MATLAB Examples + Rigid Body Dynamics For Beginners: Euler angles & Quaternions + Fundamentals of Kalman Filtering:: A Practical Approach (Progress in Astronautics and Aeronautics)
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Editorial Reviews

About the Author

Phil Kim received all the degrees (BS, MS, and PhD) in Aerospace Engineering from Seoul National University. After his education, he worked at Korea Aerospace Research Institute as a Senior Researcher. There, his main task was to develop autonomous flight algorithm and onboard software for unmanned aerial vehicle. An on-screen keyboard program named 'Clickey' developed by him during his period in PhD program served as a bridge to bring the author currently being a Senior Research Officer at National Rehabilitation Research Institute of Korea.
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Product Details

  • Paperback: 234 pages
  • Publisher: CreateSpace Independent Publishing Platform (July 12, 2011)
  • Language: English
  • ISBN-10: 1463648359
  • ISBN-13: 978-1463648350
  • Product Dimensions: 7.4 x 0.5 x 9.7 inches
  • Shipping Weight: 15.5 ounces (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (26 customer reviews)
  • Amazon Best Sellers Rank: #309,263 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

7 of 7 people found the following review helpful By devgru on April 15, 2012
Format: Paperback Verified Purchase
This is a very good introduction and the MATLAB examples clarify things a great deal. Some of the key concepts are repeated and within a day or two its fairly easy to get a conceptual understanding of the Kalman Filter. You can easily read this book a couple times within a week...its that easy of a read. The only annoying thing is the translation...its pretty clear that the translator didnt have a strong grasp of English grammar but he gets his point across.

With that said...if you really want to understand and use the Kalman filter you are probably going to want to learn the mathematical theory behind it.
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9 of 10 people found the following review helpful By Morsi on August 23, 2011
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i'm so glad to be the first to review this wonderful book, and be the first to thank the author Phil Kim and the translator Lynn Huh, thank you guys.
the book has very practical examples implemented in matlab .m files for simulation, which i guess that this is the best way to bring the subject to life
and will enable students visualize what's going on.
examples that are included, attitude determination with Gyros and accelerometers and radar tracking.
so you should be able to get a good idea of real life implementation.

like the author said, people who will start learn the subject by learning the mathematics involved shall be probably intimidated, and i can't agree more.
the author approaches the subject from implementation point of view, because i guess to implement the kalman filter is much easier than understanding the derivation of the filter, then after you understand how to use it and play with it, most probably the learning the math should be much easier.

recommended books to supplement this book:
1- "Fundamentals of Kalman filtering: a practical approach" by Paul Zarchan is an excellent book on the subject and some how has same approach like this book, but it is a humongous book.
2- "optimal state estimation" by Dan Simon, very mathematical and requires strong math muscles.
3-if you are after theory, steven Kay's series of books are a good start point.

at the end this is an excellent pedagogical book and i am sure it is going to be a best selling among its peers.
to make this review helpful for others, i would like to invite you to watch those motivating videos:
[...]

[...]
bottom line:
highly recomended for any body who really wants to learn how to apply kalman filter and get a smooth ride introduction.
good luck.
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5 of 5 people found the following review helpful By Kenneth C. Crandall on February 18, 2012
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A very comfortable read for me. The author gives the 50,000 foot perspective first and then drills into the details of Kalman filtering with clear examples. This certainly beats most expositions that force the reader to get the big picture after much grappling with details; often with no clear examples, just mathematical proofs. I highly recommend this book. The "Korenglish" is amusing. The asides on "isn't this easy" let me know that the author must have had his own initial frustrations with the topic!
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4 of 4 people found the following review helpful By Jordan McBain on January 13, 2013
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This is an amazing book to start learning the Kalman Filter. I have been floundering around trying to find a good resource to gain a good practical understanding of the subject; while Zarchan's book is quite good, I believe that this book should form the basis of any beginning effort in understand or *applying* the Kalman Filter. The book's explanations and examples will put the reader in a position to make use of this technology in very short order.

Comments from other reviewers about the book's "KorEnglish" are largely without merit. There are some very minor translations problems that almost always arise when the author/interpreter try to use "cute" expressions. These expressions are almost always used to soften the blow of learning the theory and I can do nothing but applaud them for their efforts in this respect. There are no translation problems that will inhibit the reader from absorbing this book's contents.

In my view of the state of Kalman filter textbooks, there remains a need for a resource that pedagogically explains the underlying mathematics of the Kalman filter (which this book largely omits in order to keep a focus on practical application).

The books by Kim and Zarchan constitute a good starting place for this complicated subject matter. I heartily recommend this book.
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10 of 13 people found the following review helpful By bob on May 23, 2013
Format: Paperback
this treatment of Kalman filtering has some useful introductory perspective to make "all those equations" seem less intimidating. Overall I found the content to be uneven and too superficial. Book also had significant noticeable typo and grammatical errors that really detracted from the presentation.

There's always a balance between simplified explanations with more intuitive feel for a broader audience, and the more detailed issues of stability, convergence, computational accuracy etc. for implementing real filters. My experience with real-world systems that don't behave as nicley as problem set examples is that applying simple intuitive reduced set equations is not sufficient. systems simple enough to yiled great results with basic filtering equations typically lend themselves to the suite of standard time series techniques. Trying to exploit the power of adaptive filtering without "all those equations" creates more problems than solutions.

On balance I found the length and price of this book too much for the value I found.
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