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size: 15,072 bytes

date: 19.07.2012

Continuous-time ARMA Identification

Hagai Kirshner, Biomedical Imaging Group, EPFL.


This collection of Matlab functions estimates continuous-time autoregressive moving-average (ARMA) parameters from sampled data. The proposed approach uses exact evaluation of the discrete-domain power-spectrum for calculating the likelihood function of the sampled data. The continuous-time model is assumed to be Gaussian and no constraints are imposed on the sampling interval value. The likelihood function exhibits several local minima and the proposed algorithm aims at finding the global one by using multiple initial conditions.

The files are:

Local minima of the likelihood function are obtained by initializing with different initial parameters, as shown here for the case of an AR(2) process with two complex poles


H. Kirshner, S. Maggio, M. Unser, "A Sampling Theory Approach for Continuous ARMA Identification," IEEE Transactions on Signal Processing, vol. 59, no. 10, pp. 4620-4634, October 2011

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