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Off-line estimation of dynamic systems parameters in the presence of outliers in autoregressive noise
Volume 4, Issues 1-2 (1993), pp. 94–110
Rimantas Pupeikis  

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https://doi.org/10.3233/INF-1993-41-206
Pub. online: 1 January 1993      Type: Research Article     

Published
1 January 1993

Abstract

In the previous paper (Pupeikis, 1992) the problem of off-line estimation of dynamic systems parameters in the presence of outliers in observations have been considered, when the filter generating an additive noise has a very special form. The aim of the given paper is the development, in such a case, of classical generalized least squares method (GLSM) algorithms for off-line estimation of unknown parameters of dynamic systems. Two approaches using batch processing of the stored data are worked out. The first approach is based on the application of S-, H-, W- algorithms used for calculation of M-estimates, and the second one rests on the replacement of the corresponding values of the sample covariance and cross-covariance functions by their robust analogues in respective matrices of GLSM and on a further application of the least squares (LS) parameter estimation algorithms. The results of numerical simulation by IBM PC/AT (Table 1) are given.

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Keywords
dynamic system parameter estimation covariance analysis outlier robustness

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INFORMATICA

  • Online ISSN: 1822-8844
  • Print ISSN: 0868-4952
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