Methods for Estimating State and Measurement Noise Covariance Matrices: Aspects and Comparison

The paper deals with estimation of noise covariance matrices in state and measurement equations of linear discrete-time stochastic dynamic systems. In the last decade several novel methods for noise covariance matrices estimation, which are based on state estimation techniques, have been proposed. Unfortunately, the novel methods have been compared mainly with classical methods proposed in the seventies only. The aim of the paper is to analyse identifiability of state noise parameters by means of the Bayesian approach and to summarise and compare the novel methods from both theoretical and numerical point of view.