Amazon.com: Fundamentals of Kalman Filtering: A Practical Approach (Progress in Astronautics and Aeronautics) (9781563474552): Paul Zarchan, Howard Musoff: Books

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Fundamentals of Kalman Filtering: A Practical Approach (Progress in Astronautics and Aeronautics) [Hardcover]

Paul Zarchan (Author), Howard Musoff (Author)
5.0 out of 5 stars  See all reviews (4 customer reviews)


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Hardcover, February 2001 --  

Book Description

February 2001 Progress in Astronautics and Aeronautics (Book 190)
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, 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.


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About the Author

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.


Product Details

  • Hardcover: 664 pages
  • Publisher: Amer Inst of Aeronautics & (February 2001)
  • Language: English
  • ISBN-10: 1563474557
  • ISBN-13: 978-1563474552
  • Product Dimensions: 9.3 x 6.4 x 1.7 inches
  • Shipping Weight: 2.5 pounds
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (4 customer reviews)
  • Amazon Best Sellers Rank: #2,222,872 in Books (See Top 100 in Books)

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5.0 out of 5 stars (4 customer reviews)
 
 
 
 
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19 of 20 people found the following review helpful:
5.0 out of 5 stars information, December 8, 2005
This review is from: Fundamentals of Kalman Filtering: A Practical Approach (Progress in Astronautics and Aeronautics) (Hardcover)
Although I am disappointed in the second reviewers comments he is wrong concerning the code. Complete code is presented in the text and is on CD for all of the examples. Readers can now make up their own mind before purchasing the text. If you go to the Google Book Search and type in Zarchan you will be able to read the entire text. I suggest people who have previously been terrified of Kalman filters and want to learn how to build one, read the Introduction and possibly the second Chapter to see if the text meets their needs and is in accordance with their individual learning styles. I know that many readers have found the text useful.
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10 of 10 people found the following review helpful:
5.0 out of 5 stars A great book, February 22, 2005
This review is from: Fundamentals of Kalman Filtering: A Practical Approach (Progress in Astronautics and Aeronautics) (Hardcover)
This book builds up the concepts needed to understand Kalman filtering from very basic ideas like least squares, and tries to keep things grounded in practical examples. It is, as advertised, the practical approach, totally unpretentious, and clearly written.

The commenter referred to as the 'second commenter' in the author's comment has apparently withdrawn his comment and is not me.
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7 of 8 people found the following review helpful:
5.0 out of 5 stars A well written and practical textbook, September 22, 2006
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Neil Milligan (Perth, Australia) - See all my reviews
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This review is from: Fundamentals of Kalman Filtering: A Practical Approach (Progress in Astronautics and Aeronautics) (Hardcover)
I purchased this book primarily to gain some basic understanding of kalman filtering, but have found the book so interesting and engaging that I have taken the time to rewrite the code to pascal and run the examples etc, as presented in the book. Being a systems engineer by education, but not by profession, the approach used in the book allowed me to apply some of my previous but unused training very efficiently.

The practical approach, along with just enough notation to be rigorous, makes for a thoughtful read, but is still easily understood. When combined with the examples, the alternative approaches presented and the code it achieves exactly what the authors state as their objectives.
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Inside This Book (learn more)
First Sentence:
IN THIS chapter we will try to discuss all of the numerical techniques used throughout the text. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
adding process noise, downrange location, inverse ballistic coefficient, receiver downrange, linearized measurement equation, covariance matrix representing errors, exact fundamental matrix, measurement noise matrix, downrange error, more noise transmission, zero process noise, constant velocity receiver, polar differential equations, downrange velocity, initial covariance matrix, initial projectile velocity, system dynamics matrix, theoretical error bounds, measurement noise standard deviation, covariance matrix projections, receiver altitude, truncation error formulas, white process noise, filter divergence, white noise with spectral density
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Number of Measurements Fig, Simulation Theory, Cartesian Kalman, Theory Simulation, New York, Applied Optimal Estimation, Massachusetts Inst, Run Number Fig, Technology Press, Downrange Fig, Deg Launch, Standard Mathematical Tables, States Estimated
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