Deterministic sampling for propagating model covariance

Abstract

Deterministic sampling can be used for nonlinear propagation of the statistics of signal processing models. Unlike Monte Carlo methods, random generators are not utilized in any stage. The samples are instead calculated deterministically. Our novel approach generalizes the deterministic sampling technique for propagating covariance in the unscented Kalman filter by introducing generic excitation matrices describing small discrete canonical ensembles. The approximation lies in how well the available statistical information is encoded in the discrete ensemble, not how each sample is propagated. The application and performance of deterministic sampling are illustrated for a typical step response analysis of an electrical device modeled with an uncertain digital filter.