Model-Based Parameter Estimation in Computational Electromagnetics - Edmund K Miller
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Présentation Model - Based Parameter Estimation In Computational Electromagnetics de Edmund K Miller Format Relié
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Résumé : 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.
Biographie: Edmund K. Miller earned a Ph.D. in electrical engineering from the University of Michigan in 1965 with an emphasis on computational electromagnetics. His working career has been varied, including employment at four universities (Michigan Technological University, University of Michigan, Kansas University and Ohio University), three companies (MB Associates, Rockwell International Science Center, and General Research Corporation, all in California) and two national laboratories (Lawrence Livermore and Los Alamos). He was the first president of ACES. He is a life fellow of IEEE and an ACES fellow.
Sommaire: Computational electromagnetics models the interaction of electromagnetic fields with physical objects and their environment, such as radar waves with airplanes. Model Based Parameter Estimation (MDPE) and System Identification (SID) seek to construct a model, based on solutions to Maxwell's equations, and then use optimization to minimise the discrepancy between modelled and observed data. The goal is a pragmatic mathematical relation, or simplified model, between input and output data without understanding and modelling the details of all physical processes. This approach makes it easier to find frequency and angle dependencies of (for instance) radar cross sections and is thus key to airspace security in dynamic situations. After an introduction to the subject and the mathematical background, chapters will cover System Identification, MBPE and the use of Prony's Methods in computational electromagnetics, sparse as well as derivative sampling, pattern synthesis and estimation, accuracy of measurements and models, as well as associated 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.
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