A direct determination of ARMA algorithms for the simulation of stationary random processes

An efficient technique to determine autoregressive moving average (ARMA) algorithms for simulating realizations of multivariate random processes with a specified (target) spectral matrix is presented. The computation of the parameters of the ARMA model is accomplished by relying on the minimization of frequency domain errors. The need to perform a spectral factorization is discussed and some new algorithms to perform this operation are presented. Finally, the features of this technique are critically assessed and examples of application are given in context with nonlinear mechanics problems.