Subspace algorithms for the stochastic identification problem
Automatica (Journal of IFAC)
On the Kalman-Yakubovich-Popov lemma
Systems & Control Letters
SIAM Review
Vector ARMA estimation: a reliable subspace approach
IEEE Transactions on Signal Processing
Sensor array processing based on subspace fitting
IEEE Transactions on Signal Processing
Brief Performance evaluation of methods for identifying continuous-time autoregressive processes
Automatica (Journal of IFAC)
Brief Identification of continuous-time AR processes from unevenly sampled data
Automatica (Journal of IFAC)
Modeling continuous-time processes via input-to-state filters
Automatica (Journal of IFAC)
Linear LMS compensation for timing mismatch in time-interleaved ADCs
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Hi-index | 22.14 |
In this paper, we study the problem of reconstructing a continuous-time (CT) model from an identified discrete-time (DT) model for a continuous-time stochastic process. We present a new necessary and sufficient condition for the existence of the solution. We also show that the solution is unique if it exists. Our results are useful in modeling multivariable processes as well. These results are then used to develop an algorithm where the intermediate discrete-time model estimation is not necessary. The performance of our algorithm is illustrated using numerical simulations.