Model-Based Parameter Estimation in Computational Electromagnetics

Model-Based Parameter Estimation in Computational Electromagnetics Synopsis

Computational electromagnetics (CEM) involves modeling the interaction of electromagnetic fields with physical objects and their environment, such as the radiation emitted by antennas and the fields scattered from radar targets.

First-principles or generating models (GMs) based on Maxwell's equations, provide a microscopic, spatial description of the charge and current distributions that normally require several samples per wavelength. Model-based parameter estimation (MBPE) uses a macroscopic, reduced-order, physically based fitting model (FM) to adaptively sample GM results while minimizing the number needed to quantify various EM observables such as frequency responses, far-field radiation patterns, interaction effects, etc.

The FMs can reduce the needed GM sampling cost by a factor of 10 or more while yielding a continuous result of needed observables to avoid missing important details. The FMs can also indicate the numerical uncertainty of such quantities from measured as well as computed data.

After an introduction to the subject and its mathematical background, subsequent chapters cover system identification, MBPE techniques and the various roles of Prony's methods as FMs in CEM. Other related topics that are covered include derivative sampling, radiation pattern synthesis and estimation, and assorted other applications.

The book is aimed at the computational electromagnetics community and those working in applied sciences with complex models such as acoustics, mechanical structures, geo-physics and physics.