Fixed Interval Smoothing for State Space Models

  • Howard L. Weinert

Table of contents

  1. Front Matter
    Pages i-x
  2. Howard L. Weinert
    Pages 1-12
  3. Howard L. Weinert
    Pages 13-28
  4. Howard L. Weinert
    Pages 29-67
  5. Howard L. Weinert
    Pages 69-80
  6. Howard L. Weinert
    Pages 81-97
  7. Back Matter
    Pages 99-119

About this book


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.


Signal acoustics communication filters information metrics model signal processing statistics

Authors and affiliations

  • Howard L. Weinert
    • 1
  1. 1.Johns Hopkins UniversityUSA

Bibliographic information

  • DOI
  • Copyright Information Kluwer Academic Publishers 2001
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-5680-6
  • Online ISBN 978-1-4615-1691-0
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site