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Fundamentals of Kalman Filtering: A Practical Approach
Fundamentals of Kalman Filtering: A Practical Approach
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Fundamentals of Kalman Filtering: A Practical Approach

Author: Paul Zarchan and Howard Musoff
Item# 1145

A practical guide to building Kalman filters showing how filtering equations can be applied to real-life problems. Numerous examples are presented in detail showing the many ways in which Kalman filters can be designed. The book contains a CD-ROM of Kalman filtering software. 765 pages, hard cover . View Full Description

Category: Kalman Filter & Integration
Publisher: AIAA
Manufacturer Number: 1563476940
 
 
 
 
Price: $124.95

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Fundamentals of Kalman Filtering: A Practical Approach, Second Edition

Paul Zarchan and Howard Musoff

This text is a practical guide to building Kalman filters and shows how the filtering equations can be applied to real-life problems. Numerous examples are presented in detail, showing the many ways in which Kalman filters can be designed. Computer code written in FORTRAN, MATLAB[registered], and True BASIC accompanies all of the examples so that the interested reader can verify concepts and explore issues beyond the scope of the text.

Sometimes mistakes are introduced intentionally to the initial filter designs to show the reader what happens when the filter is not working properly. The text spends a great deal of time setting up a problem before the Kalman filter is actually formulated to give the reader an intuitive feel for the problem being addressed. Real problems are seldom presented in the form of differential equations and they usually do not have unique solutions. Therefore, the authors illustrate several different filtering approaches for tackling a problem. Readers will gain experience in software and performance tradeoffs for determining the best filtering approach for the application at hand.

The second edition has two new chapters and an additional appendix. In the first new chapter, a recursive digital filter known as the fading memory filter is introduced and it is shown that for some radar tracking applications the fading memory filter can yield similar performance to a Kalman filter at far less computational cost. A second new chapter presents techniques for improving Kalman filter performance. Included is a practical method for preprocessing measurement data when there are too many measurements for the filter to utilize in a given amount of time. The chapter also contains practical methods for making the Kalman filter adaptive. A new appendix has been added which serves as a central location and summary for the text's most important concepts and formulas.

 

About the Authors

Paul Zarchan has a BSEE degree from the City College of New York and an MSEE degree from Columbia University. He has more than 30 years' experience in the missile guidance and control field and has worked as Principal Engineer for Raytheon, served as Senior Research Engineer with the Israeli Ministry of Defense and is currently a Principal Member of the Technical Staff at the Charles Stark Draper Laboratory.

Dr. Howard Musoff has a BSEE degree from the City College of New York, an MSEE degree from Northeastern University, and an Sc.D.degree from MIT. He is a Principal Member of the Technical Staff at the Charles Stark Draper Laboratory, were he has been employed for more than 40 years. Among other tasks, he designs Kalman filters for applications in the filed of inertial navigation. Dr. Musoff is also a co-holder of two patents in that field.


Publisher: AIAA 
Copyright: 2005
Pages: 765
ISBN-10: 1563476940
ISBN-13: 978-1563476945