This paper proposes an efficient algorithm for identifying FIR models when also the input is assumed as affected by additive noise. This procedure is more accurate than instrumental variables approaches and, differently from total least squares, does not require the a priori knowledge of the ratio between the input and output noise variances. The accuracy of the whole procedure has been tested by means of Monte Carlo simulations and compared with that of compensated and total least squares ones.

A new approach for identifying noisy input-output FIR models

DIVERSI, ROBERTO;GUIDORZI, ROBERTO;SOVERINI, UMBERTO
2008

Abstract

This paper proposes an efficient algorithm for identifying FIR models when also the input is assumed as affected by additive noise. This procedure is more accurate than instrumental variables approaches and, differently from total least squares, does not require the a priori knowledge of the ratio between the input and output noise variances. The accuracy of the whole procedure has been tested by means of Monte Carlo simulations and compared with that of compensated and total least squares ones.
Proceedings of the 3rd IEEE International Symposium on Communications, Control and Signal Processing
1548
1552
R. Diversi; R. Guidorzi; U. Soverini
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/61308
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