New design of fixed-lag smoother using covariance information in linear discrete-time stochastic systems

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This paper proposes recursive least-squares fixed-lag smoothing and filtering algorithms in linear discrete-time stochastic systems. The estimators require the information of the factorized autocovariance function of the signal. Namely, the estimation algorithms use the observation matrix, the system matrix for the state variable and the crossvariance function between the state variable and the signal. Also, it is assumed that the variance of the white observation noise is known.