Maximum-Likelihood Identification of Sampled Gaussian Processes

This work considers sampled data of continuous-domain Gaussian processes. We derive a maximum-likelihood estimator for identifying autoregressive moving average parameters while incorporating the sampling process into the problem formulation. The proposed identification approach introduces exponential models for both the continuous and the sampled processes. We construct a likelihood function from a digitally-filtered version of the available data which is asymptotically exact. This function has several local minima that originate from aliasing, plus a global minimum that corresponds to the maximum-likelihood estimator. We further compare the performance of the proposed algorithm with other currently available methods.