抄録
Recursive updating algorithms of error covariance matrices in subspace identification methods for time-varying systems are derived. The proposed algorithms can be applied to estimate the system parameters which are slowly time-varying. The algorithms are based on the fact that the subspace extraction amounts to computing singular value decomposition of the SC of the input submatrix in data product moments and the SC can be interpreted as the least squares residuals. We have proposed an unified framework for the MOESP type of the subspace identification method by using the SC. In this paper, we show the aforementioned time-varying case can be also treated in the proposed framework.
