Model-Based Parameter Estimation in Computational Electromagnetics

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.