Introduction to Random Signals and Applied Kalman Filtering, 3rd Edition (Book only) [Paperback]

Robert Grover Brown (Author), Patrick Y. C. Hwang (Author)

Book Description

ISBN-10: 0471128392 | ISBN-13: 978-0471128397 | Publication Date: November 28, 1996 | Edition: 3

In this updated edition the main thrust is on applied Kalman filtering. Chapters 1-3 provide a minimal background in random process theory and the response of linear systems to random inputs. The following chapter is devoted to Wiener filtering and the remainder of the text deals with various facets of Kalman filtering with emphasis on applications. Starred problems at the end of each chapter are computer exercises. The authors believe that programming the equations and analyzing the results of specific examples is the best way to obtain the insight that is essential in engineering work.

Product Details

• Paperback: 496 pages
• Publisher: Wiley; 3 edition (November 28, 1996)
• Language: English
• ISBN-10: 0471128392
• ISBN-13: 978-0471128397
• Product Dimensions: 9.8 x 6.8 x 1.6 inches
• Shipping Weight: 2.2 pounds
• Average Customer Review: 3.9 out of 5 stars  See all reviews (9 customer reviews)
• Amazon Best Sellers Rank: #112,780 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

28 of 29 people found the following review helpful:

4.0 out of 5 stars Excellent Intro to KF, easy on the rigor.., August 1, 1999

By A Customer

This review is from: Introduction to Random Signals and Applied Kalman Filtering, 3rd Edition (Book only) (Paperback)

This is the third edition of an introductory text on Kalman Filtering. It is easy to read, easy to follow: it presents the discrete-time and the continuous-to-discrete KFs in a clear and logical manner. The MATLAB exercises didn't seem to be integrated with the text too well, and they were written in MATALB v 4., so there are a few "corrections" that must be made ("randn" in v. 5, so all the references to "rand" in the MATLAB code must be fixed). The extended KF and some implementation issues (UDU filter, sequential estimation) are not covered as well as other topics. If you are new to the topic and are looking for a good introduction this is the book. If you already know the topic, I'd pass on the text. What I'd like to see is a new edition of Gelb, replete with MATLAB implementations..

8 of 9 people found the following review helpful:

5.0 out of 5 stars what you need to get started, July 4, 2003

By 

K. McLaughlin (USA) - See all my reviews
(REAL NAME)   

This review is from: Introduction to Random Signals and Applied Kalman Filtering, 3rd Edition (Book only) (Paperback)

I have read over simplified and over complicated descriptions
of the Kalman filter for years. The theoretical discussion is
well matched to the examples. THe authors have obviously had
extensive experiance TEACHING to a wide range of students and
the book benefits from their experiance.

I was able to program filters for three of the examples that
most closely match my own applications and exploit what I
learned in a matter of hours. The MATLAB code was useful, but
not critical.

3.0 out of 5 stars OK intro text, poor as a reference, January 21, 2012

By 

Harmon (New Hampshire) - See all my reviews

Amazon Verified Purchase

This review is from: Introduction to Random Signals and Applied Kalman Filtering, 3rd Edition (Book only) (Paperback)

I give this text 3 stars, because even though it is easy to read, it falls short of both categories that make any textbook useful: It is neither a great introductory text on the material, nor an outstanding reference for those with a good handle on estimation theory.

I used this book last semester for a graduate controls course on state estimation, and found the examples easy to read, but rather disjointed when considered as a whole. The introductory material on probability theory was incomplete, and I found that I was constantly turning to Gelb and to Mendel's "Lessons in Estimation Theory.." to get the full picture. The book also gives little treatment to modeling, which is paramount to building an effective controller.

The book does have it's merits: In addition to the typical derivation of the Kalman Filter, it provides an alternate derivation for the filter when all you have are the noisy measurements. This is a useful derivation for practical applications.