The unscented kalman filtering in extended noise environments

Abstract

This paper introduces an extended environment for the unscented Kalman filtering that considers also the presence of additive noise on input observations in order to solve the problem of optimal estimation of noise-corrupted input and output sequences. This environment includes as sub-cases both errors-in-variables filtering and unscented Kalman filtering. The unscented Kalman filtering to the presence of additive noise on input observations is considered, and is used to solve the problem of optimal estimation of noise-corrupted input and output sequences. A Monte Carlo simulation shows that the performance of the unscented Kalman filtering technique leads to the expected minimal variance estimates.