the CT input $$u(t) = \sin(0.714t)+ \sin(1.428t)+\sin(2.142t). \notag$$ The noiseless output is computed analytically by assuming that it corresponds to the output of the system at the stationary regime, i.e., $$x(t) = \sum_{k=1}^3|G^*(i\omega_k)|\sin(\omega_k t+\angle G^*(i\omega_k)), \notag$$ where $(\omega_1,\omega_2,\omega_3) = (0.714, 1.428,2.142)[\textnormal{rad}/\textnormal{s}]$. This output is sampled at $h = 0.3[\textnormal{s}]$ and is contaminated with additive noise, which is set as an i.i.d. Gaussian sequence of variance 0.1. Sixty different sample sizes are considered, ranging logarithmically from $N=100$ to $N=25500$, and 300 Monte Carlo runs are performed for each value of $N$.

Figures \[fig1\] and \[fig2\] show the sample means and sample mean square errors (MSEs) of each estimated parameter. The SRIVC-c estimator accurately identifies all parameters while the standard SRIVC method fails to recover the true parameter vector as $N$ increases. The MSEs for the SRIVC-c estimator decrease to zero as expected for consistency, while at least two out of the three estimated parameters given by the SRIVC method are biased, which empirically indicates that the SRIVC estimator is not consistent in this example.

Multisine input: Different sampling periods
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We now study the effect of the intersample behaviour on the SRIVC-type estimates. Under the same input and noise variance as 