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Kalman Filtering : Theory and Practice Using MATLAB [Hardcover]

Mohinder S. Grewal (Author), Angus P. Andrews (Author)
2.7 out of 5 stars  See all reviews (3 customer reviews)


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Book Description

0471392545 978-0471392545 January 16, 2001 2
". . . an authentic magnum opus worth much more than its weight in gold!"-IEEE Transactions on Automatic Control, from a review of the First Edition
"The best book I've seen on the subject of Kalman filtering . . . Reading other books on Kalman filters and not this one could make you a very dangerous Kalman filter engineer."-Amazon.com, from a review of the First Edition
In this practical introduction to Kalman filtering theory and applications, authors Grewal and Andrews draw upon their decades of experience to offer an in-depth examination of the subtleties, common problems, and limitations of estimation theory as it applies to real-world situations. They provide many illustrative examples drawn from an array of application areas including GPS-aided INS, the modeling of gyros and accelerometers, inertial navigation, and freeway traffic control. In addition, they share many hard-won lessons about, and original methods for, designing, implementing, validating, and improving Kalman filters, including techniques for:
* Representing the problem in a mathematical model
* Analyzing estimator performance as a function of model parameters
* Implementing the mechanization equations in numerically stable algorithms
* Assessing computational requirements
* Testing the validity of results
* Monitoring filter performance in operation
As the best way to understand and master a technology is to observe it in action, Kalman Filtering: Theory and Practice Using MATLAB(r), Second Edition includes companion software in MATLAB(r), providing users with an opportunity to experience first hand the filter's workings and its limitations.
This updated and revised edition of Grewal and Andrews's classic guide is an indispensable working resource for engineers and computer scientists involved in the design of aerospace and aeronautical systems, global positioning and radar tracking systems, power systems, and biomedical instrumentation.

An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.



Editorial Reviews

Review

"This book could serve as an introduction to stochastic/random processes...practicing engineers with enough mathematical background would appreciate this book...academicians and scientists would also find this book very useful." (IEEE Circuits & Devices Magazine, July 2003)

"Provides readers with a working familiarity with both the theoretical and practical aspects of Kalman filtering." (SciTech Book News, Vol. 25, No. 3, September 2001)

From the Back Cover

". . . an authentic magnum opus worth much more than its weight in gold!"
from a review of the First Edition — IEEE Transactions on Automatic Control

"The best book I've seen on the subject of Kalman filtering . . . Reading other books on Kalman filters and not this one could make you a very dangerous Kalman filter engineer."
from a review of the First Edition — Amazon.com

In this practical introduction to Kalman filtering theory and applications, authors Mohinder Grewal and Angus Andrews draw upon their decades of experience to offer an in-depth examination of the subtleties, common problems, and limitations of estimation theory as it applies to real-world situations. They provide many illustrative examples drawn from an array of application areas including GPS-aided INS, the modeling of gyros and accelerometers, inertial navigation, and freeway traffic control. In addition, they share many hard-won lessons about, and original methods for, designing, implementing, validating, and improving Kalman filters, including techniques for:

  • Representing the problem in a mathematical model
  • Analyzing estimator performance as a function of model parameters
  • Implementing the mechanization equations in numerically stable algorithms
  • Assessing computational requirements
  • Testing the validity of results
  • Monitoring filter performance in operation

As the best way to understand and master a technology is to observe it in action, Kalman Filtering: Theory and Practice Using MATLAB®, Second Edition includes companion software in MATLAB®, providing users with an opportunity to experience firsthand the filter's workings and its limitations.

This updated and revised edition of Grewal and Andrews's classic guide is an indispensable working resource for engineers and computer scientists involved in the design of aerospace and aeronautical systems, global positioning and radar tracking systems, power systems, and biomedical instrumentation.


Product Details

  • Hardcover: 416 pages
  • Publisher: Wiley-Interscience; 2 edition (January 16, 2001)
  • Language: English
  • ISBN-10: 0471392545
  • ISBN-13: 978-0471392545
  • Product Dimensions: 9.4 x 6.2 x 1.1 inches
  • Shipping Weight: 1.7 pounds
  • Average Customer Review: 2.7 out of 5 stars  See all reviews (3 customer reviews)
  • Amazon Best Sellers Rank: #1,633,767 in Books (See Top 100 in Books)

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Average Customer Review
2.7 out of 5 stars (3 customer reviews)
 
 
 
 
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48 of 52 people found the following review helpful:
1.0 out of 5 stars Kalman what???, November 27, 2002
By A Customer
Amazon Verified Purchase(What's this?)
This review is from: Kalman Filtering : Theory and Practice Using MATLAB (Hardcover)
I have to say this is a strange book on Kalman filtering - the structure of the book is probably as incoherent as the presentation. It is not a good book for a student wishing to learn Kalman Filtering and how to adapt it to practical problems.

I think the authors assume that the readers all know about Kalman filters and as far as introductions are concerned, only need to know the history of it. Indeed it is not until chapter 4.2 that you get any acknowledgement of the actual filter the rest of the book is peripheral information that would apply if you were a seasoned Kalman Filter expert.

The weird thing is that anyone with a detailed knowledge of Kalman Filtering would certainly know everything there was to know about matrix algebra, and so Appendix B has a tutorial or refresher on matrix forms - weird.

The notation also appears non standard and there are many sections where equation variables are not well explained and the examples very obsure and the premable non existent.

This is not a good book for any one wishing to either familiarise themselves with Kalman filtering - there is some good information in this but it is not a text book by any stretch of the imagination.

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17 of 17 people found the following review helpful:
2.0 out of 5 stars Not enough examples..., March 19, 2004
By A Customer
This review is from: Kalman Filtering : Theory and Practice Using MATLAB (Hardcover)
This book is difficult to understand due to the lack of simple examples to illustrate keep points as they are introduced.

There are a few examples in each chapter, however the authors skip too many steps and make you constantly flip around in the book to review previous examples upon which he bases the new examples. The end result is just wasteful trashing by the student.

This is a good book, but it does't have enough textbook examples to be used for an intro class in Kalam filtering.

Maybe this book would be useful as a secondary reference for a person that already knows Kalman filtering.

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4 of 24 people found the following review helpful:
5.0 out of 5 stars Most useful text on the subject, December 2, 2002
By A Customer
This review is from: Kalman Filtering : Theory and Practice Using MATLAB (Hardcover)
I have been working with "Kalman Fitering, ... Using Matlab",
2nd Edition, for several weeks, and I must tell you it is by far the
most useful text on the subject.
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Inside This Book (learn more)
First Sentence:
Theoretically the Kalman Filter is an estimator for what is called the linear-quadratic problem, which is the problem of estimating the instantaneous "state" (a concept that will be made more precise in the next chapter) of a linear dynamic system perturbed by white noise-by using measurements linearly related to the state but corrupted by white noise. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
observational update, measurement sensitivity matrix, dynamic coefficient matrix, measurement decorrelation, roundoff error propagation, filter measurement update, unit roundoff error, temporal update, triangularization methods, harmonic resonator, suboptimal filters, estimation loop, upper triangularization, unit upper triangular matrix, fundamental solution matrix, error budgeting, temporal epoch, process noise covariance, observability matrix, state vector components, block submatrices, computer roundoff, orthogonality principle, state transition matrices, estimation uncertainty
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Norbert Wiener, Carl Friedrich Gauss, Instrumentation Laboratory, Real World Kalman Filter Model
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