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Full Description
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.
Contents
Chapter 1: System Identification and Model-Based Parameter Estimation
Chapter 2: A Brief Sampling of System Identification and Model-Based Parameter Estimation Applications in Various Disciplines
Chapter 3: Mathematical Background of MBPE
Chapter 4: Sampling Strategies for Effective Implementation of Prony's Method
Chapter 5: Conserving Waveform Information Content in the Spectral Domain using Prony's Method
Chapter 6: Minimizing the Number of Frequency Samples Needed to Represent a Transfer Function Using Adaptive Sampling
Chapter 7: Using Prony's Method to Design Arrays that Produce the Patterns of Continuous Source Distributions and Prescribed Radiation Patterns
Chapter 8: Designing Discrete Arrays Using Prony's Method to Model Exponentiated Radiation Patterns
Chapter 9: Using Adaptive Estimation to Minimize the Number of Samples Needed to Develop a Radiation or Scattering Pattern to a Specified Uncertainty
Chapter 10: Modeling Dipole Arrays that Produce Synthesized Patterns Using NEC
Chapter 11: Using Model-Based Parameter Estimation to Assess the Accuracy of Numerical Models
Chapter 12: Using Prony's Method to Develop Pole-Based Models of Linear Sources
Chapter 13: Inversion of One-Dimensional Scattering Data Using Prony's Method
Chapter 14: Derivative Sampling of Computational Data
Appendix A: MBPE estimation in computational electromagnetics
Appendix B: Symbols and Notation
Appendix C: MBPE References
