Kendall's advanced theory of statistics
Kendall's advanced theory of statistics
Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
Continuous-time frequency domain subspace system identification
Signal Processing - Special issue: subspace methods, part II: system identification
Automatica (Journal of IFAC)
Analyses, Development, and Applications of TLS Algorithms in Frequency Domain System Identification
SIAM Journal on Matrix Analysis and Applications
Extended Subspace Identification of Improper Linear Systems
Proceedings of the conference on Design, automation and test in Europe - Volume 1
Brief paper: An insight into instrumental variable frequency-domain subspace identification
Automatica (Journal of IFAC)
Identification of nonlinear systems using Polynomial Nonlinear State Space models
Automatica (Journal of IFAC)
Brief paper: Two nonlinear optimization methods for black box identification compared
Automatica (Journal of IFAC)
Hierarchical gradient-based identification of multivariable discrete-time systems
Automatica (Journal of IFAC)
Hi-index | 22.15 |
In the general case of non-uniformly spaced frequency-domain data and/or arbitrarily coloured disturbing noise, the frequency-domain subspace identification algorithms described in McKelvey, Akcay, and Ljung (IEEE Trans. Automatic Control 41(7) (1996) 960) and Van Overschee and De Moor (Signal Processing 52(2) (1996) 179) are consistent only if the covariance matrix of the disturbing noise is known. This paper studies the asymptotic properties (strong convergence, convergence rate, asymptotic normality, strong consistency and loss in efficiency) of these algorithms when the true noise covariance matrix is replaced by the sample noise covariance matrix obtained from a small number of independent repeated experiments. As an additional result the strong convergence (in case of model errors), the convergence rate and the asymptotic normality of the subspace algorithms with known noise covariance matrix follows.