A frequency-domain approach for estimating continuous-time diffusively coupled linear networks

TL;DR

This paper introduces a frequency-domain method for accurately estimating physical components in continuous-time diffusively coupled linear networks, leveraging network symmetry and noise covariance for improved parameter identification.

Contribution

The paper presents a novel three-step frequency-domain identification approach that exploits network symmetry and extends to subnetworks, enabling accurate component estimation with minimal measurements.

Findings

Successfully estimates component values in complex networks

Uses noise covariance to improve estimation accuracy

Effective with limited measurements and single excitation

Abstract

This paper addresses the problem of consistently estimating a continuous-time (CT) diffusively coupled network (DCN) to identify physical components in a physical network. We develop a three-step frequency-domain identification method for linear CT DCNs that allows to accurately recover all the physical component values of the network while exploiting the particular symmetric structure in a DCN model. This method uses the estimated noise covariance as a non-parametric noise model to minimize variance of the parameter estimates, obviating the need to select a parametric noise model. Moreover, this method is extended to subnetworks identification, which enables identifying the local dynamics in DCNs on the basis of partial measurements. The method is illustrated with an application from In-Circuit Testing of printed circuit boards. Experimental results highlight the method's ability to consistently estimate component values in a complex network with only a single excitation.