Optimal Filtering

by John B. Moore, Brian D.O. Anderson, John B. Moore

Book Overview

This graduate-level text augments and extends studies of signal processing, particularly in regard to communication systems and digital filtering theory. Topics include filtering, linear systems, and estimation; the discrete-time Kalman filter; time-invariant filters; properties of Kalman filters; computational aspects; smoothing of discrete-time signals; and more. 24 figures. 1979 edition.

• Format: Paperback
• Language: English
• ISBN: 0486439380
• ISBN13: 9780486439389
• Release Date: January 2005
• Publisher: Dover Publications
• Length: 357 Pages
• Weight: 1.16 lbs.
• Dimensions: 0.7" x 6.6" x 9.2"

Customer Reviews

Optimal Filtering (Dover Books on Engineering)

I'm sorry I have not fluency in English, however I can say that I'm satisfied with the book "Optimal Filtering (Dover Books on Engineering)" and your service. thank you.

Classics in Signal Processing

This book is already a classics in signal processing. If you are serious in optimal control, linear estimation or general signal processing, get this book to have a look, you might be benefited from it. I am glad to see Dover reprint this out of print classics at reasonable price.

Excellent textbook in extended Kalman filtering

It is very useful to study the Bayesian optimal filtering.

Excellent work on filtering and statistical signal processing

This is an excellent book. I often see it referenced, in the old edition from Prentice Hall, in IEEE papers written by "old important guys" (and gals of course) indicating that it carries some weight amongst people in the know. It treats aspects of filtering, from the ground up, in a mathematically correct way. You do need to be comfortable with matrix analysis, calculus, certainly random processes, and have some level of "mathematical sophistication" (that elusively defined quality.) As a supplement to a course in Statistical Signal Processing where you use the book by, say, Kay, it would be very good (and much cheaper than Kay.) I'm not sure how Dover selects their catalogue of books, but they certainly do a good job of picking up the lagged copyright from big publishers, of very good older books. The book is written by a couple of Aussies who begin each chapter with the salutation "G'day Mate!" (**) and end each chapter with "Good on yer Cobber," and also, confusingly, refer to each other as "Bruce" throughout the book. The book is good enough for you to be able to overlook these nationalistic quirks. They also use tracking problems, in the section on Kalman filtering, taken from Aussie Rules football, as a player tracks the ball through the air, before he is clobbered by an opponent. This is a good example of tracking, whether you are a Raytheon missile engineer, or an Aussie rules footballer. They even use a more complicated example where the player tracks both the ball and the other player (the clobberer). This example could probably be generalized to missile defense. In summary, this really is a good book on filtering, especially the core material of Wiener filtering and Kalman filtering. Highly recommended. Good on yer Cobbers!! (Bruce and Bruce, that is.) **Disclaimer: Some of this review is a fictionalised account of a review.

Solid Mathematical Analysis

Originally published in 1979, this is a graduate level text book on signal processing. Filtering out the unwanted data is a major part of signal processing. A returning radar image, for instance, contains an awful lot of data, while all you really want is the blip that identifies where the airplane is in the sky. The remaining static has to be filtered out. This book is rigerous mathematical treatment of filtering. It is not a cookbook on how to build filters, but instead describing the basic fundamental background of the concepts behine filtering. As stated, this is intended for use at graduate school level. It goes well beyond what would be expected at the undergraduate engineering level. The mathematics are basically calculus, natrix definition and manipulation, and probability.