Perspectives on errors-in-variables estimation for dynamic systems

  • Authors:

  • Torsten Söderström;Umberto Soverini;Kaushik Mahata

  • Affiliations:

  • Department of Systems and Control, Information Technology, Uppsala University, P.O. Box 337, SE-75105 Uppsala, Sweden;DEIS-Dipartimento di Elettronica, Informatica e Sistemistica, Universita di Bologna, Viale Risorgimento 2, IT-40136 Bologna, Italy;Department of Systems and Control, Information Technology, Uppsala University, P.O. Box 337, SE-75105 Uppsala, Sweden

  • Venue:
  • Signal Processing
  • Year:
  • 2002

Quantified Score

Hi-index 0.11

Visualization

Abstract

The paper gives all overview of various methods for identifying dynamic errors-in-variables systems. Several approaches are classified by how the original information in time-series data of the noisy input and output measurements is condensed before further processing. For some methods, such as instrumental variable estimators, the information is condensed into a nonsymmetric covariance matrix as a first step before further processing. In a second class of methods, where a symmetric covariance matrix is used instead, the Frisch scheme and other bias-compensation approaches appear. When dealing with the estimation problem in the frequency domain, a milder data reduction typically takes place by first computing spectral estimators of the noisy input-output data. Finally, it is also possible to apply maximum likelihood and prediction error approaches using the original time-domain data in a direct fashion. This alternative will often require quite high computational complexity but yield good statistical efficiency. The paper is also presenting various properties of parameter estimators for the errors-in-variables problem, and a few conjectures are included, as well as some perspectives and experiences by the authors.