A^*(p) &= a_1^* p^{n^*} + a_2^* p^{{n^*}-1} + \dots + a_{n^*}^* p+1. \notag\end{aligned}$$ Suppose that the CT input $u(t)$ is known from $t=t_1$ to $t=t_N$, and that $N$ noisy measurements of the output $x(t)$ are obtained at the instants $\{t_k\}_{k=1}^N$. In other words, the output observations are given by $$\label{outputdescription}
y(t_k) =x(t_k) + v(t_k), \quad k=1,\dots,N,$$ where it is assumed that the sampled noise sequence $\{v(t_k)\}$ can be described as a zero-mean and finite variance random process. Due to the nature of the sampled signals and the difficulty of computing the time-derivative of CT white noise, which does not have finite variance [@aastrom2012introduction], we only consider DT noise in this paper.

To identify the system, we propose the model structure $$G(p)= \frac{b_0 p^{m} + b_1 p^{m-1} + \dots + b_m}{a_1 p^{n} + a_2 p^{n-1} + \dots + a_n p+1},  \notag$$ where the parameter vector $$\theta := \begin{bmatrix}
a_1, & a_2, &\dots, & a_n, & b_0, & b_1, &\dots, & b_m 
\end{bmatrix}^\top \notag$$ needs to be estimated. The goal is to obtain an accurate model of the CT system $G^*(p):=B^*(p)/A^*(p)$ given the knowledge of $N$ samples of the output measurements and the CT input signal. Note that in this framework the 