input signal is not limited to hold reconstructions. Hence, the description includes the standard framework where $u(t)$ is assumed to be obtained through a ZOH or FOH and extends to more general inputs, such as multisines and band limited signals [@schoukens1994advantages].

The identification of the system $G^*(p)$ can be done by obtaining the data points $\{u(t_k),y(t_k)\}$ and applying a method for CT system identification, such as in [@chen2013refined], or as in [@young1980refined; @maruta2013projection; @gonzalez2018asymptotically] for regular sampling schemes. In most of these algorithms, however, the hold reconstructions of the input and output are assumed, and they are independent of the exact nature of the signals. In this work, we show that the knowledge of the exact intersample behaviour of the input can provide further insights for a better design of the identification procedure.

The Simplified Refined Instrumental Variable method for Continuous-time systems (SRIVC) {#sec3}
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The SRIVC estimator is an adaptive instrumental variable algorithm where parameter-dependent CT filters are updated iteratively. In each step, the instruments are computed using the parameter estimate obtained in the previous iteration until the model parameters have converged. The iterative procedure of the SRIVC algorithm is designed so that the sum of squares of the 