This function simulate a FAR(1) process with a Wiener noise. As for
the simul.wiener, the function use the Karhunen-Loève
expansion of the noise. The FAR(1) process, defined by its linear
operator (see far for more details), is computed in the
Karhunen-Loève basis then projected in the natural basis. The
parameters given in input (d.rho and cst1) are expressed
in the Karhunen-Loève basis.
The linear operator, expressed in the Karhunen-Loève basis, is of the
form:
$$
\left(\begin{array}{cc}%
\code{d.rho} & 0 \cr%
0 & eps.rho
\end{array}\right)
$$
Where d.rho is the matrix provided in ths call, the two 0 are
in fact two blocks of 0, and eps.rho is a diagonal matrix having on
his diagonal the terms:
$$\left(\varepsilon_{k+1}, \varepsilon_{k+2}, \ldots, %
\varepsilon_{\code{m2}}\right)$$
where
$$\varepsilon_{i}=\frac{\code{cst1}}{i^2}+%
\frac{1-\code{cst1}}{e^i}$$
and k is the length of the d.rho diagonal.
The d.rho matrix can be viewed as the information and the
eps.rho matrix as a perturbation. In this logic, the norm of eps.rho
need to be smaller than the one of d.rho.