New design of fixed-interval smoother using covariance information in linear stochastic continuous-time systems

摘要

This paper proposes recursive least-squares fixed-interval smoothing and filtering algorithms in linear continuous-time stochastic systems. The estimators require the information of the autocovariance function of the signal, the variance of white observation noise and the observed value. The autocovariance function of the signal is expressed in the form of the semi-degenerate kernel.