Passivity-based sample selection and adaptive vector fitting algorithm for pole-residue modeling of sparse frequency-domain data

An adaptive sampling and modeling technique is presented for accurate broadband modeling of highly dynamic systems, based on a sparse set of support samples. The method is numerically more stable than conventional approaches, while desired physical properties such as system stability, causality and even passivity can be imposed. The algorithm adaptively selects a quasi-optimal sample distribution and model complexity. During the modeling process, no prior knowledge of the system´s dynamics is used.