This paper describes a new approach for identifying FIR models from a finite number of measurements, in the presence of additive and uncorrelated white noise. In particular, two different frequency domain algorithms are proposed. The first algorithm is based on some theoretical results concerning the dynamic Frisch scheme. The second algorithm maps the FIR identification problem into a quadratic eigenvalue problem. Both methods resemble in many aspects some other identification algorithms, originally developed in the time domain. The features of the proposedmethods are compared with each other and with those of some time domain algorithms by means of Monte Carlo simulations.

Frequency domain identification of FIR models from noisy input-output data

Soverini, Umberto
;
2019

Abstract

This paper describes a new approach for identifying FIR models from a finite number of measurements, in the presence of additive and uncorrelated white noise. In particular, two different frequency domain algorithms are proposed. The first algorithm is based on some theoretical results concerning the dynamic Frisch scheme. The second algorithm maps the FIR identification problem into a quadratic eigenvalue problem. Both methods resemble in many aspects some other identification algorithms, originally developed in the time domain. The features of the proposedmethods are compared with each other and with those of some time domain algorithms by means of Monte Carlo simulations.
Soverini, Umberto; Söderström, Torsten
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/727951
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