Estimation, control, and the discrete Kalman filter
 Responsibility
 Donald E. Catlin.
 Language
 English.
 Imprint
 New York : SpringerVerlag, c1989.
 Physical description
 xiii, 274 p. : ill. ; 25 cm.
 Series
 Applied mathematical sciences (SpringerVerlag New York Inc.) ; v. 71.
Access
Available online
Math & Statistics Library
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Call number  Status 

QA402.3 .C37 1989  Unknown 
SAL3 (offcampus storage)
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Call number  Status 

QA402.3 .C37 1989  Available 
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Creators/Contributors
 Author/Creator
 Catlin, Donald E.
Contents/Summary
 Bibliography
 Bibliography: p. [264]265.
 Contents

 Contents: Basic Probability. Minimum Variance Estimation  How the Theory Fits. The Maximum Entropy Principle. Adjoints, Projections, Pseudoinverses. Linear Minimum Variance Estimation. Recursive Linear Estimation (Bayesian Estimation). The Discrete Kalman Filter. The Linear Quadratic Tracking Problem. Fixed Interval Smoothing. Appendices AG. Bibliography. Index.
 (source: Nielsen Book Data)
 Publisher's Summary
 This is a one semester text for students in mathematics, engineering, and statistics. Most of the work that has been done on Kalman filter was done outside of the mathematics and statistics communities, and in the spirit of true academic parochialism was, with a few notable exceptions, ignored by them. This text is Catlin's small effort toward closing that chasm. For mathematics students, the Kalman filtering theorem is a beautiful illustration of Functional Analysis in action; Hilbert spaces being used to solve an extremely important problem in applied mathematics. For statistics students, the Kalman filter is a vivid example of Bayesian statistics in action.
(source: Nielsen Book Data)
Subjects
Bibliographic information
 Publication date
 1989
 Series
 Applied mathematical sciences ; v. 71
 Note
 Includes index.
 ISBN
 038796777X (alk. paper)
 9780387967776 (alk. paper)
 354096777X
 9783540967774