Modelorder reduction in electromagnetics using modelbased parameter estimation

This chapter outlines and demonstrates the use of model-based parameter estimation (MBPE) in electromagnetics. MBPE can be used to circumvent the requirement of obtaining all samples of desired quantities (e.g., impedance, gain, RCS) from a first-principles model (FPM) or from measured data (MD) by instead using a reduced-order, physically based approximation of the sampled data called a fitting model (FM). One application of a FM is interpolating between (pole-series FMs), and/or extrapolating from (exponential-series FMs), samples of FPM or MD observables to reduce the amount of data that is needed. A second is to use a FM in FPM computations by replacing needed mathematical expressions with simpler analytical approximations to reduce the computational cost of the FPM itself. As an added benefit, the FMs can be more suitable for design and optimization purposes than the usual numerical data that comes from a FPM or MD because the FMs can normally be handled analytically rather than via operations on the numerical samples. Attention here is focused on the use of FMs that are described by exponential and pole series, and how data obtained from various kinds of sampling procedures can be used to quantify such models, i.e., to determine numerical values for their coefficients.