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Fundamentals of Kalman Filtering: A Practical Approach by H. Musoff; P. Zarchan
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CircaEUR 72,23
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“Very Good condition with minimal signs of wear. No sign of underlining, notes, or highlights in ”... Maggiori informazioniinformazioni sulla condizione
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Oggetto che si trova a: Las Cruces, New Mexico, Stati Uniti
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Numero oggetto eBay:146498497400
Specifiche dell'oggetto
- Condizione
- Ottime condizioni
- Note del venditore
- Book Title
- Fundamentals of Kalman Filtering: A Practical Approach
- Book Series
- Progress in Astronautics and Aeronautics Series
- Narrative Type
- Nonfiction
- Country/Region of Manufacture
- United States
- ISBN
- 9781563476945
Informazioni su questo prodotto
Product Identifiers
Publisher
American Institute of Aeronautics & Astronautics
ISBN-10
1563476940
ISBN-13
9781563476945
eBay Product ID (ePID)
44781532
Product Key Features
Number of Pages
765 Pages
Language
English
Publication Name
Fundamentals of Kalman Filtering : a Practical Approach
Publication Year
2005
Subject
Functional Analysis, Probability & Statistics / Stochastic Processes, Mechanics / Dynamics, Robotics, Aeronautics & Astronautics
Type
Textbook
Subject Area
Mathematics, Technology & Engineering, Science
Series
Progress in Astronautics and Aeronautics Ser.
Format
Hardcover
Dimensions
Item Height
1.9 in
Item Weight
42.5 Oz
Item Length
9.2 in
Item Width
6.6 in
Additional Product Features
Edition Number
2
Intended Audience
Trade
LCCN
2006-270071
Dewey Edition
21
Series Volume Number
Vol. 208
Illustrated
Yes
Dewey Decimal
629.8312
Synopsis
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. 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. MATLAB is a registered trademark of The MathWorks, Inc., 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. 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 facling memory filter can yield similar performance to a Kalman filter at for 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 containspractical 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.
LC Classification Number
TL507.P75 vol.208
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