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Kalman Filter for Beginners: with MATLAB Examples

4.0 out of 5 stars 27 customer reviews
ISBN-13: 978-1463648350
ISBN-10: 1463648359
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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: 1.2 pounds (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (27 customer reviews)
  • Amazon Best Sellers Rank: #228,720 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

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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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:
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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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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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Format: Paperback
The Kalman Filter is one of the most important algorithms developed in the 20th century. It is the centerpiece of modern spaceflight and inertial guidance systems. One would think that in today's day and age there would be a bevy of texts explaining the basics of the filter at an undergraduate level. Unfortunately this is not the case. The reason is simple: To understand the filter you really should have already taken courses in linear algebra, signals and systems (including state space), and probability and random processes.
However, this book makes a good effort toward making the basic idea of the Kalman filter accessible to readers with a limited background. This book will not make
you an expert in this area (by any stretch) but will at least give you the proper orientation and the big picture toward further study. It does not attempt to demonstrate the
optimality of the filter and its relation to least squares/minimum variance estimation.
All in all I commend the author for his effort. However, the book would be so much better if a co-author proficient in English had done the
final editing. The book is still understandable and can be digested but is far from optimal. This is why I have deducted one and one half stars (rating = 3.5).

What other options does the Kalman Filter neophyte have at his/her disposal for learning this material? The best source
I have found are the notes by Peter Joesph. Joesph worked with Kalman and is one of the founding fathers of this area. His notes are available at his personal website which can be found via a simple web search.
Another nice introductory textbook is

1. Fundamentals of Kalman Filtering by Zarchan and Musoff.
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