Abstract
This paper proposes an extension of the framework of linear time invariant system identification to linear slowly time-varying system identification. The class of systems considered is described by ordinary differential equations with coefficients varying piecewise polynomially with time. The estimation is performed in the frequency domain using multisine excitation signals within an errors-in-variables framework. It is also discussed how multisine excitations provide estimates of, on the one hand, the speed of variation of the system and, on the other hand, an estimation of a non-parametric noise model.
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