On the Generation of Correlated Gaussian Random Variates by Inverse DTF

In this paper the problem of generating a stationary band-limited Gaussian random vector with arbitrary complex autocorrelation by the inverse discrete Fourier transform (IDTF) algorithm is considered. Instead of using the classical frequency mask (FM), determined from samples of the (band-limited) target power spectral density (PSD) of the process, a new FM is obtained by matching the autocorrelation obtained with the IDFT algorithm to a desired arbitrary autocorrelation. Example results presented show that the new FM is able to significantly increase the autocorrelation accuracy of the generated process at no additional online computational cost.