Subspace-based rational interpolation from phase data

In this paper, a subspace-based identification algorithm to identify stable linear-time-invariant systems from corrupted phase samples of the frequency response function on nonuniformly spaced grid of frequencies are developed. The algorithm is strongly consistent if the corruptions are zero-mean random variables with a known covariance function. Furthermore, it exactly retrieves finite-dimensional systems from noise-free phase data using a finite amount of data.