Fixed Interval Smoothing for State Space Models

Howard L Weinert

ISBN 10: 0792372999 / ISBN 13: 9780792372998

Published by Springer Us Feb 2001, 2001

About this Item

Neuware - Fixed-interval smoothing is a method of extracting useful information from inaccurate data. It has been applied to problems in engineering, the physical sciences, and the social sciences, in areas such as control, communications, signal processing, acoustics, geophysics, oceanography, statistics, econometrics, and structural analysis. 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. Fixed Interval Smoothing for State Space Models will be the primary source for those wanting to understand and apply fixed-interval smoothing: academics, researchers, and graduate students in control, communications, signal processing, statistics and econometrics.

Seller Inventory # 9780792372998

Bibliographic Details

• Title: Fixed Interval Smoothing for State Space Models
• Publisher: Springer Us Feb 2001
• Publication Date: 2001
• Language: English
• ISBN 10: 0792372999
• ISBN 13: 9780792372998
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 13D millimeters 241 millimeters width by 160 millimeters height by 13 millimeters depth
• Weight: 383 grams

About this title

Synopsis

Fixed-interval smoothing is a method of extracting useful information from inaccurate data. It has been applied to problems in engineering, the physical sciences, and the social sciences, in areas such as control, communications, signal processing, acoustics, geophysics, oceanography, statistics, econometrics, and structural analysis.
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.

Fixed Interval Smoothing for State Space Models will be the primary source for those wanting to understand and apply fixed-interval smoothing: academics, researchers, and graduate students in control, communications, signal processing, statistics and econometrics.

Review

`In the reviewer's opinion, this monograph is pioneering in a fascinating and relatively new field of research. It should prove useful to people working in control theory and doing research on smoothing, and for those who want to choose a smoothing algorithm for a particular application.'
Zdzislaw W. Trzaska, American Mathematical Society