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Virginia Polytechnic Institute and State University

Measurement covariance-constrained estimation for poorly modeled dynamic systems

Abstract

dc:description.abstract

An optimal estimation strategy is developed for post-experiment estimation of discretely measured dynamic systems which accounts for system model errors in a much more rigorous manner than Kalman filter-smoother type methods. The Kalman filter-smoother type methods, which currently dominate post-experiment estimation practice, treat model errors via “process noise", which essentially shifts emphasis away from the model and onto the measurements. The usefulness of this approach is subject to the measurement frequency and accuracy. The current method treats model errors by use of an estimation strategy based on concepts from optimal control theory. Unknown model error terms are explicitly included in the formulation of the problem and estimated as a part of the solution. In this manner, the estimate is improved; the model is improved; and an estimate of the model error is obtained. Implementation of the current method is straightforward, and the resulting state trajectories do not contain jump discontinuities as do the Kalman filter-smoother type estimates. Results from a number of simple examples, plus some examples from spacecraft attitude estimation, are included. The current method is shown to obtain significantly more accurate estimates than the Kalman filter-smoother type methods in many of the examples. The difference in accuracy is accentuated when the assumed model is relatively poor and when the measurements are relatively sparse in time and/or of low accuracy. Even for some well-modeled, densely measured applications, the current method is shown to be competitive with the Kalman filter-smoother type methods.

Degree

thesis:*
Name thesis:degree_name
Ph. D.
Level thesis:degree_level
doctoral
Discipline thesis:degree_discipline
Engineering Mechanics
Department dc:contributor.department
Engineering Mechanics
Grantor dc:publisher
Virginia Polytechnic Institute and State University
Year dc:date.issued
1985

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Mook, Daniel Joseph
Chair dc:contributor.committeechair
  • Junkins, J.L.
Committee members dc:contributor.committeemember
  • Cliff, Eugene M.
  • Hendricks, Scott L.
  • Kraige, Luther
  • Telionis, Demetrios P.

Rights

dc:rights
Statement dc:rights
  • In Copyright

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/10919/49776
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/49776

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
related terms
citation

Mook, Daniel Joseph. Measurement covariance-constrained estimation for poorly modeled dynamic systems. doctoral thesis, Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/49776