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Kalman Filter for Beginners: with MATLAB Examples (Englisch) Taschenbuch – 12. Juli 2011

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Über den Autor und weitere Mitwirkende

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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Format: Taschenbuch Verifizierter Kauf
Der Autor nimmt den Leser bei der Hand, versichert mehrmals "keine Angst, der Kalman Filter tut nicht weh und beißt nicht" und erklärt Schritt für Schritt sowohl die Idee als auch die Berechnung des Filters. Das macht er recht gut. Die Beispiele sind auch sehr gut in aufsteigender Schwierigkeit angeordnet. Es wird ein simpler Matlab Kode abgedruckt. Kim erklärt auch an Hand dieses Kodes noch einmal, was gemacht wird. Es ist nicht der kürzeste, aber der einfachste Weg zum Verständnis des Filters.

Das Buch ist ursprünglich in Koreanisch bei einem Koreanischen Verlag erschienen. Das Original verdient wahrscheinlich 5 Sterne. Leider hält man die im Amazon-Eigenverlag erschienene Englische "Übersetzung" in der Hand. Laut Cover ist der Übersetzer Lynn Huh ein erfahrener Mann. Der Text hat aber nur sehr entfernt mit Englisch zu tun. Es fehlen z.B. fast durchgehend die Artikel, die Satzstellung ist falsch. Wahrscheinlich gibt es im gesamten Buch keinen einzigen vollständig korrekten Satz. Ich wollte schon zum Lesen aufhören. Aber man gewöhnt sich auch an diese Art von Sprache und am Ende ist es mir gar nicht mehr so aufgefallen.
Eine Katastrophe sind auch die Diagramme.Wahrscheinlich sind sie im Original in Farbe. Im Amazon Druck sind sie in Grau. Einige Diagramme erinnern eher an das weiße Quadrat auf weißen Grund von Malewitsch als an ein Diagramm. Man sieht außer den Koordinatenachsen gar nix.
Es ist ein Jammer wie ein an und für sich guter Text herausgeberisch misshandelt wurde.
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Amazon.com: HASH(0x9003139c) von 5 Sternen 27 Rezensionen
7 von 7 Kunden fanden die folgende Rezension hilfreich
HASH(0x9003fed0) von 5 Sternen Excellent introduction 15. April 2012
Von devgru - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
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.
9 von 10 Kunden fanden die folgende Rezension hilfreich
HASH(0x90042138) von 5 Sternen Big Thanks for the Author 23. August 2011
Von Morsi - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
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.

Note:
amazon deleted the youtube links in my original review, please go to youtube and search for student dave kalman tutorials part 1,2 and 3.
good luck fellows.
6 von 6 Kunden fanden die folgende Rezension hilfreich
HASH(0x900420fc) von 5 Sternen Kalman filtering made simple. 18. Februar 2012
Von Kenneth C. Crandall - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
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!
5 von 5 Kunden fanden die folgende Rezension hilfreich
HASH(0x900423f0) von 5 Sternen Amazing 13. Januar 2013
Von Jordan McBain - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
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.
4 von 4 Kunden fanden die folgende Rezension hilfreich
HASH(0x90042450) von 5 Sternen A good try but needs further editing 19. August 2014
Von sp - Veröffentlicht auf Amazon.com
Format: Taschenbuch
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. This book is written by industry folks (Lincoln Labs/Draper Labs)who actually designed Kalman Filters
and is quite basic and gives a good practical grounding in Kalman Filtering. Many books on inertial navigation also have basic discussions of the Kalman Filter.
A particularly good one can be found in the excellent book by Groves "Principles of Navigation Systems".

At the intermediate level I like Dan Simons Optimal State Estimation, Crassidis and Junkins Optimal Estimation of Dynamic Systems, and Gelb's Applied Optimal Estimation.

The best advanced (graduate level) book is hands down
Maybeck "Stochastic Models, Estimation, and Control Volume 1". This is the place to go to really understand the mathematical
details of the filter. A nice benefit is that Chapter 1 presents the idea of the filter in an intuitive manner that can be understood by anyone with
a basic grounding in probability.

Other very good advanced texts include Anderson and Moore Optimal Filtering and
Kailath's Linear Estimation ( a tour-de-force of the underlying theory for the very serious student). As another reviewer also pointed out
the 2 volumes by Kay on Statistical Signal Processing are also very worthwhile. In fact, Kay's text on Probability is also a good book for the
Kalman Filter neophyte to learn the basics required to read the intermediate and advanced books mentioned above.

Good Luck !
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