@article{5519439d2d8a42acacc9fbc1dadde691,
title = "On maximum likelihood estimation of continuous-time oscillators modelled as continuous-time autoregressive system",
abstract = "In this paper, we address the problem of identifying a continuous-time oscillator. We use a continuous-time autoregressive model to represent the oscillators. We asume that only discrete-time measurements are available, from which we obtain the oscillator equivalent discrete-time model in terms of the continuous-time model parameters. We identify the model using the Maximum Likelihood method.",
keywords = "Adaptive Optics, Continuous-time model, Maximum Likelihood, Oscillators identification, Vibrations",
author = "Maria Coronel and Karen Gonzalez and Pedro Escarate and Rodrigo Carvajal and Aguero, {Juan Carlos}",
note = "Funding Information: La notaci{\'o}n que se utilizar{\'a} en el presente trabajo se define a continuaci{\'o}n: AT denota la matriz transpuesta de A, eA representa a la matriz exponencial de la matriz A. Se utiliza el siguiente vector columna para representar una sub-secuencia de la se{\~n}al xt, xk:j = [xkT xk+1T ··· xlT]T. θ0 denota el valor verdadero del par{\'a}metro θ y θˆ denota una estimaci{\'o}n de θ0. θˆ(m) denota la estimaci{\'o}n de θ0 en la m-{\'e}sima iteraci{\'o}n. E{·} denota el operador esperanza. E {x|y} denota el valor esperado de la variable aleatoria x dado la variable aleatoria y. Publisher Copyright: {\textcopyright} 2003-2012 IEEE.",
year = "2019",
month = jul,
doi = "10.1109/TLA.2019.8931211",
language = "English",
volume = "17",
pages = "1214--1219",
journal = "IEEE Latin America Transactions",
issn = "1548-0992",
number = "7",
}