From time series to linear system-part I. Finite dimensional linear time invariant systems
Automatica (Journal of IFAC)
From time series to linear system-Part II. Exact modelling
Automatica (Journal of IFAC)
From time series to linear system—Part III. Approximate modelling
Automatica (Journal of IFAC)
The Frisch scheme in dynamic system identification
Automatica (Journal of IFAC) - Identification and system parameter estimation
Invariants and canonical forms for systems structural and parametric identification
Automatica (Journal of IFAC)
Papers: Identification of stochastic linear systems in presence of input noise
Automatica (Journal of IFAC)
Brief Optimal errors-in-variables filtering
Automatica (Journal of IFAC)
Brief paper: Maximum likelihood identification of noisy input-output models
Automatica (Journal of IFAC)
Survey paper: Errors-in-variables methods in system identification
Automatica (Journal of IFAC)
The unscented Kalman filtering in extended noise environments
ACC'09 Proceedings of the 2009 conference on American Control Conference
Brief Optimal errors-in-variables filtering
Automatica (Journal of IFAC)
Technical Communique: Linear dynamic filtering with noisy input and output
Automatica (Journal of IFAC)
Hi-index | 22.15 |
This paper deals with optimal (minimal variance) filtering in an errors-in-variables framework. Differently from many other contexts, errors-in-variables models treat all variables in a symmetric way (no partition of the variables into inputs and outputs is required) and assume additive noise on all the variables. The filtering technique described in this paper can be easily implemented in a recursive way and does not require the use of a Riccati equation at every update. The results of Monte Carlo simulations have shown the effectiveness and consistency of the approach.