Estimation of continuous-time stochastic signals from sample covariances

The problem of estimating the parameters in continuous-time stochastic signals, represented by continuous-time autoregressive moving average (CARMA) processes, from discrete-time data is analyzed. It is shown that the variances are close to the Cramer-Rao bound for certain choices of the sampling interval and the number of covariance elements used in the criterion function.