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Advanced Kalman Filtering, Least-Squares and Modeling: A Practical Handbook, by Bruce P. Gibbs
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This book provides a complete explanation of estimation theory and application, modeling approaches, and model evaluation. Each topic starts with a clear explanation of the theory (often including historical context), followed by application issues that should be considered in the design. Different implementations designed to address specific problems are presented, and numerous examples of varying complexity are used to demonstrate the concepts.
This book is intended primarily as a handbook for engineers who must design practical systems.� Its primary�goal is to explain all important aspects of Kalman filtering and least-squares theory and application.� Discussion of estimator design and model development is emphasized so that the reader may develop an estimator that meets all application requirements and is robust to modeling assumptions.� Since it is sometimes difficult to a priori determine the best model structure, use of exploratory data analysis to define model structure is discussed.� Methods for deciding on the "best" model are also presented.
A second goal is to present little known extensions of least squares estimation or Kalman filtering that provide guidance on model structure and parameters, or make the estimator more robust to changes in real-world behavior.
A third goal is discussion of implementation issues that make the estimator more accurate or efficient, or that make it flexible so that model alternatives can be easily compared.
The fourth goal is to provide the designer/analyst with guidance in evaluating estimator performance and in determining/correcting problems.
The final goal is to provide a subroutine library that simplifies implementation, and flexible general purpose high-level drivers that allow both easy analysis of alternative models and access to extensions of the basic filtering.
- Sales Rank: #2032647 in Books
- Published on: 2011-03-01
- Original language: English
- Number of items: 1
- Dimensions: 10.30" h x 1.50" w x 7.10" l, 3.05 pounds
- Binding: Hardcover
- 632 pages
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The only book to cover least-squares estimation, Kalman filtering, and model development
This book provides a complete explanation of estimation theory and application, modeling approaches, and model evaluation. Each topic starts with a clear explanation of the theory (often including historical context), followed by application issues that should be considered in the design. Different implementations designed to address specific problems are presented, and numerous examples of varying complexity are used to demonstrate the concepts.
It focuses on practical methods for developing and implementing least-squares estimators, Kalman filters, and newer filtering techniques. Since model development is critical to a successful implementation, the book discusses first-principle approaches, basis function expansions, stochastic models, and ARMA-type structures. Computation of empirical models and determination of "best" model structures and order are also discussed. The text is written to help the reader design an estimator that meets all application requirements.
Specifically addressed are methods for developing models that meet estimation goals, procedures for making the estimator robust to modeling and numerical errors, extensions of the basic methods for handling non-ideal systems, and techniques for evaluating performance and analyzing accuracy problems. Including many real-world examples, the book:
- Presents little-known extensions of least-squares estimation and Kalman filtering that provide guidance on model structure and parameters
- Explains numerical accuracy, computational burden, and modeling tradeoffs for real-world applications
- Discusses implementation issues that make the estimator more accurate or efficient, or that make it flexible so that model alternatives can be easily compared
- Offers guidance in evaluating estimator performance and in determining/correcting problems
- A related Web site provides a subroutine library that simplifies implementation, as well as general purpose high-level drivers that allow for the easy analysis of alternative models and access to extensions of the basic Kalman filtering
Drawing from four decades of the author's experience with the material, Advanced Kalman Filtering, Least-Squares and Modeling is a comprehensive and detailed explanation of these topics. Practicing engineers, designers, analysts, and students using estimation theory to develop practical systems will find this a very useful reference.
About the Author
BRUCE P. GIBBS has forty-one years of experience applying estimation and control theory to applications for NASA, the Department of Defense, the Department of Energy, the National Science Foundation, and private industry. He is currently a consulting scientist at Carr Astronautics, where he designs image navigation software for the GOES-R geosynchronous weather satellite. Gibbs previously developed similar systems for the GOES-NOP weather satellites and GPS.
Most helpful customer reviews
0 of 0 people found the following review helpful.
wow
By S. Rowe
Gibbs's exposition shames academic texts, ironic given that he's spent his career in private industry. The syntax is clear and direct (unlike "Estimation with Applications to Tracking and Navigation"). The explanations are derivation, not proof, driven. What a breath of fresh air. I don't know what inspired Gibbs to publish what seems to be an exquisite diary on this subject, but it is truly a gift to future generations. As a controls student I've found it priceless. If anything, it's too comprehensive. I don't need to see an estimator derived from multiple perspectives (ML,MAP,MMSE,LS), but Gibbs shows it anyways. I don't need to see multiple solutions algorithms, QR seems good enough! But these are "anti-complaints." Bravo Gibbs. Before buying this be sure to closely study linear algebra and probability theory.
0 of 0 people found the following review helpful.
one of the best written textbooks I have ever encountered.
By Amazon Customer
This is one of the best written textbooks I have ever encountered. The extensive referencing of prior work is is mind boggling. This book
captures the state of the art in a form that can be readily absorbed and advanced by future generations. This book is destined to be a classic.
I am glad Mr Gibbs has clearly presented the various square root methods. With today's and future
generation computers these methods are likely to fall out of use … and then be rediscovered when the
solution of a truly gigantic, poorly conditioned estimation problem is urgently needed to retrieve
astronauts stranded on Mars. Then this material will become “the suibject area bible”.
0 of 0 people found the following review helpful.
Outstanding – complete and comprehensive
By Ken Ernandes
The most thorough and complete work I have seen on the subject. This provides a lot of in-depth information and insight into various areas not found elsewhere. While quite a bit of theory is presented, the main concentration is providing practical information useful for a wide variety of filter implementations. This will be most useful for somebody with a strong mathematical background, particularly in linear algebra, who is looking for a comprehensive understanding and the best solution for a particular application.
See all 4 customer reviews...
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