Estimation of noise covariance matrices for periodic systems

摘要

Estimation of the noise covariance matrices for linear time-variant stochastic dynamic periodic systems is treated. The novel offline method for estimation of the covariance matrices of the state and measurement noises is designed. The method is based on analysis of second-order statistics of the state estimate produced by the linear multi-step predictor. The estimates of the noise covariance matrices are unbiased and converge to the true values with increasing number of data. The theoretical results are illustrated in numerical examples.