The Cramér-Rao lower bound for noisy input-output systems
Signal Processing
Discrete-Time Stochastic Systems: Estimation and Control
Discrete-Time Stochastic Systems: Estimation and Control
Survey paper: Errors-in-variables methods in system identification
Automatica (Journal of IFAC)
Identifiability of errors in variables dynamic systems
Automatica (Journal of IFAC)
Uncertainty of transfer function modelling using prior estimated noise models
Automatica (Journal of IFAC)
Technical Communique: Asymptotic variance expressions for closed-loop identification
Automatica (Journal of IFAC)
From experiment design to closed-loop control
Automatica (Journal of IFAC)
On the frequency domain accuracy of closed-loop estimates
Automatica (Journal of IFAC)
Finite model order accuracy in Hammerstein model estimation
Automatica (Journal of IFAC)
Hi-index | 22.15 |
Errors-in-variables estimation problems for single-input-single-output systems with Gaussian signals are considered in this contribution. It is shown that the Fisher information matrix is monotonically increasing as a function of the input noise variance when the noise spectrum at the input is known and the corresponding noise variance is estimated. Furthermore, it is shown that Whittle's formula for the Fisher information matrix can be represented as a Gramian and this is used to provide a geometric representation of the asymptotic covariance matrix for asymptotically efficient estimators. Finally, the asymptotic covariance of the parameter estimates for the system dynamics is compared for the two cases: (i) when the model includes white measurement noise on the input and the variance of the noise is estimated, and (ii) when the model includes only measurement noise on the output. In both cases, asymptotically efficient estimators are assumed. An explicit expression for the difference is derived when the underlying system is subject only to measurement noise on the output.