System identification: theory for the user
System identification: theory for the user
Sampling in digital signal processing and control
Sampling in digital signal processing and control
On parameter and state estimation for linear differential-algebraic equations
Automatica (Journal of IFAC)
Identification of Continuous-time Models from Sampled Data
Identification of Continuous-time Models from Sampled Data
Stochastic theory of continuous-time state-space identification
IEEE Transactions on Signal Processing
Parameter estimation for continuous-time models-A survey
Automatica (Journal of IFAC)
| Hi-index | 22.14 |
The standard continuous time state space model with stochastic disturbances contains the mathematical abstraction of continuous time white noise. To work with well defined, discrete time observations, it is necessary to sample the model with care. The basic issues are well known, and have been discussed in the literature. However, the consequences have not quite penetrated the practice of estimation and identification. One example is that the standard model of an observation, being a snapshot of the current state plus noise independent of the state, cannot be reconciled with this picture. Another is that estimation and identification of time continuous models require a more careful treatment of the sampling formulas. We discuss and illustrate these issues in the current contribution. An application of particular practical importance is the estimation of models based on irregularly sampled observations.