Subspace-based multivariable system identification using polyspectral analysis

The problem of MIMO (multi-input multi-output) system identification given noisy input-output measurements is considered. The various noise processes affecting the system are zero-mean, jointly stationary Gaussian, whereas the system operates under a non-Gaussian input. First the MIMO transfer function is estimated using the integrated polyspectrum and cross-polyspectrum of the time-domain input-output measurements. Then an existing subspace-based technique for parametric system identification given noisy measurements of the underlying transfer function, is adapted to apply to the problem under consideration. We show that the resultant parametric transfer function estimator is strongly consistent. A simulation example is provided.