Modal parameter estimation from input–output Fourier data using frequency-domain maximum likelihood identification

A multi-variable frequency-domain maximum likelihood estimator is proposed to identify the modal parameters together with confidence intervals directly from the input–output Fourier data. The use of periodic excitation signals enables the use of a so-called non-parametric errors-in-variables noise model for an accurate description of the measurement set-up. The combination with a maximum likelihood identification approach yields a solver that is extremely robust to errors in the data, such as noise and leakage and hence results in accurate models. Since the maximum likelihood approach involves an optimization problem, a least-squares estimator is proposed as well, with the availability of a stabilization diagram. Both algorithms have been optimized for modal analysis applications by a significant reduction of the computation time and memory requirements. In the case when random noise excitation is required, the proposed method allows a parametric compensation for effects of leakage.