MODEL-BASED PARAMETER-ESTIMATION IN ELECTROMAGNETICS - PART I - BACKGROUND AND THEORETICAL DEVELOPMENT

Electromagnetics (EM), as is true of other scientific disciplines, uti lizes solution tools that range from rigorous analytical formulations to approximate, engineering estimates. One goal in EM, especially for design applications where specific performance is desired, is that of expending only enough solution effort to obtain a quantitative result commensurate with the problem requirements. Included among the possibl e approaches for achieving such a goal is model-based parameter estima tion (MBPE). MBPE is used to circumvent the requirement of obtaining a ll samples of desired observables (e.g., impedance, gain, RCS, etc.) f rom a first-principles model (FPM) or from measured data (MD) by inste ad using a reduced-order, physically-based approximation of the sample d data, called a fitting model (FM). One application of a fitting mode l is interpolating between, or extrapolating from, samples of first-pr inciples-model or measured data observables, to reduce the amount of d ata needed. A second is to use a fitting model in first-principles-mod el computations by replacing needed mathematical expressions with simp ler analytical approximations, to reduce the computational cost of the first-principles model itself. As an added benefit, the fitting model s can be more suitable for design and optimization purposes than the u sual numerical data that comes from a first-principles model or measur ed data, because the fitting models can normally be handled analytical ly rather than via operations on the numerical samples. This article f irst provides a background and motivation for using MBPE in electromag netics, focusing on the use of fitting models that are described by ex ponential and pole series. How data obtained from various kinds of sam pling procedures can be used to quantify such models, i.e., to determi ne numerical values for their coefficients is also presented. It conti nues by illustrating applications of MBPE to various kinds of EM obser vables. It concludes by discussing how MBPE might be used to improve t he efficiency of first-principles models based on frequency-domain int egral equations (IEs).