Parametric identification of two-port models in the frequency domain

The authors treat the problem of parametric estimation of linear time-invariant dynamic two-port models (e.g. the short-circuit admittance matrix) from experimental data. A multivariate frequency-domain Gaussian maximum likelihood estimator is proposed to estimate the unknown coefficients occurring in the rational two-port model. It takes the perturbing noise of all the measured voltages and currents into account. The covariance matrix of the noise is assumed to be known, e.g. from measurements. The estimates and their covariance matrix are obtained as the result of an optimization procedure. The value of the minimized loss function and the covariance matrix of the estimates can be used to determine the model structure. The ability of the estimator to handle real measurement problems is demonstrated by means of experimental results. Using the estimated two-part parameters of an unloaded band-pass filter, it was possible to predict the transfer function of the loaded filter within an error of ±0.01 dB on the magnitude and ±0.1° on the phase