Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-25T06:11:00.186Z Has data issue: false hasContentIssue false

A square-root fixed-interval discrete-time smoother

Published online by Cambridge University Press:  17 February 2009

Tania Prvan
Affiliation:
Dept. of Pure and Applied Mathematics, Washington State University, Pullman, 99164-2930, WA, USA.
M. R. Osborne
Affiliation:
Dept. of Statistics, Institute of Advanced Studies, Australian National University, G.P.O. Box 4, Canberra, A.C.T. 2601, Australia.
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The square-root fixed-interval discrete-time smoother has been used extensively in discrete recursive estimation since it was first developed by Rauch, Tung and Streibel [10]. Various people, for example Bierman [2], [3], have recognized the inherent instability in employing this kind of smoother in its original form; they have investigated implementing the recursion more stably. Bierman's paper [3] is one such contribution. In this paper we plan to present a more comprehensive development of Bierman's approach, and to show that this algorithm can be implemented more stably as a square-root smoother. Throughout this paper the fixed-interval discrete-time smoother will be referred to as the RTS smoother. Numerical results are given for the usual form of the RTS smoother, Bierman's algorithm and our square-root formulation of his algorithm. These confirm that the square-root formulation is more desirable than Bierman's algorithm, which performs better than the usual implementation of the RTS smoother.

Type
Research Article
Copyright
Copyright © Australian Mathematical Society 1988

References

[1]Anderson, B. D. O. and Moore, J. B., Optimal filtering (Prentice-Hall, Englewood Cliffs, N. J., 1979).Google Scholar
[2]Bierman, G. J., Estimation methods for discrete sequential estimation (Academic Press, 1977).Google Scholar
[3]Bierman, G. J., “A new computationally efficient fixed-interval, discrete time smoother”, Automatica 19 (5) (1983) 503511.CrossRefGoogle Scholar
[4]Gerig, T. M. and Gallant, A. R., “Computing methods for linear models subject to linear constraints”, J. Statist. Comput. Simulation 3 (1975) 283296.CrossRefGoogle Scholar
[5]Golub, G. H. and Van Loan, C. F., Matrix computations (John Hopkins University Press, 1983).Google Scholar
[6]Luenberger, D. G., Optimization by vector space methods (Wiley, 1969).Google Scholar
[7]Osborne, M. R. and Prvan, Tania, “On algorithms for generalised smoothing splines”, J. Austral. Math. Soc. Ser. B 29 (1988) 319338.CrossRefGoogle Scholar
[8]Osborne, M. R. and Prvan, Tania, “Smoothness and conditioning in generalised smoothing spline calculations”, J. Austral. Math. Soc. Ser. B 30 (1988) 4356.CrossRefGoogle Scholar
[9]Pandit, S. M. and Wu, S. M., Time series and system analysis with applications (Wiley, 1983).Google Scholar
[10]Rauch, H. E., Tung, F. and Streibel, C. T., “Maximum likelihood estimates of linear dynamic systems”, AIAA J. 3 (8) (1965) 14451450.CrossRefGoogle Scholar
[11]Wecker, W. E. and Ansley, C. F., “The signal extraction approach to nonlinear regression and spline smoothing”, J.A.S.A. 78 (381) (1983) 8189.CrossRefGoogle Scholar