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Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 609)
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Table of contents (5 chapters)
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Front Matter
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Back Matter
About this book
This monograph addresses problems for which a linear stochastic state space model is available, in which case the objective is to compute the linear least-squares estimate of the state vector in a fixed interval, using observations previously collected in that interval. The author uses a geometric approach based on the method of complementary models. Using the simplest possible notation, he presents straightforward derivations of the four types of fixed-interval smoothing algorithms, and compares the algorithms in terms of efficiency and applicability. Results show that the best algorithm has received the least attention in the literature.
Fixed Interval Smoothing for State Space Models:
- includes new material on interpolation, fast square root implementations, and boundary value models;
- is the first book devoted to smoothing;
- contains an annotated bibliography of smoothing literature;
- uses simple notation and clear derivations;
- compares algorithms from a computational perspective;
- identifies a best algorithm.
Reviews
Zdzislaw W. Trzaska, American Mathematical Society
Authors and Affiliations
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Johns Hopkins University, USA
Howard L. Weinert
Bibliographic Information
Book Title: Fixed Interval Smoothing for State Space Models
Authors: Howard L. Weinert
Series Title: The Springer International Series in Engineering and Computer Science
DOI: https://doi.org/10.1007/978-1-4615-1691-0
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 2001
Hardcover ISBN: 978-0-7923-7299-8Published: 28 February 2001
Softcover ISBN: 978-1-4613-5680-6Published: 24 October 2012
eBook ISBN: 978-1-4615-1691-0Published: 06 December 2012
Series ISSN: 0893-3405
Edition Number: 1
Number of Pages: X, 119
Topics: Electrical Engineering, Signal, Image and Speech Processing, Statistics, general