A scalable multi-step least squares method for network identification with unknown disturbance topology. (arXiv:2106.07548v3 [eess.SY] UPDATED)

Identification methods for dynamic networks typically require prior knowledge
of the network and disturbance topology, and often rely on solving poorly
scalable non-convex optimization problems. While methods for estimating network
topology are available in the literature, less attention has been paid to
estimating the disturbance topology, i.e., the (spatial) noise correlation
structure and the noise rank in a filtered white noise representation of the
disturbance signal. In this work we present an identification method for
dynamic networks, in which an …

arxiv identification least network scalable squares topology