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Stealth technology is a crucial pre-requisite in the combat zone,
where swiftness, surprise and initiative are the decisive elements
for survivability. The supreme goal here is to reduce the
visibility of military vehicles by shaping, application of radar
absorbing materials, passive cancellation, active cancellation etc.
With respect to multilayered radar absorbing structures (RAS), this
book presents an efficient algorithm based on particle swarm
optimization (PSO), for the material selection as well as
optimization of thickness of multilayered RAS models considering
both normal as well as oblique incidence cases. It includes a
thorough overview of the theoretical background required for the
analysis of multilayered RAS as well as the step-by-step procedure
for the implementation of PSO-based algorithm. The accuracy and
computational efficiency of the indigenously developed code is also
clearly established using relevant validations and case studies.
FEATURES Provides step-by-step procedure for the implementation of
particle swarm optimization (PSO) based algorithm in the context of
multilayered radar absorbing structures (RAS) design Helps to
understand the EM design, analysis and optimization of multilayered
RAS Describes the theoretical background required for the analysis
of multilayered RAS Illustrates in detail the theoretical
formulation supported by intuitive ray diagrams and comprehensive
flowcharts to implement the algorithm with ease Includes elaborate
validations and case studies This book will serve as a valuable
resource for students, researchers, scientists, and engineers
involved in the electromagnetic design and development of
multi-layered radar absorbing structures.
Stealth technology is a crucial pre-requisite in the combat zone,
where swiftness, surprise and initiative are the decisive elements
for survivability. The supreme goal here is to reduce the
visibility of military vehicles by shaping, application of radar
absorbing materials, passive cancellation, active cancellation etc.
With respect to multilayered radar absorbing structures (RAS), this
book presents an efficient algorithm based on particle swarm
optimization (PSO), for the material selection as well as
optimization of thickness of multilayered RAS models considering
both normal as well as oblique incidence cases. It includes a
thorough overview of the theoretical background required for the
analysis of multilayered RAS as well as the step-by-step procedure
for the implementation of PSO-based algorithm. The accuracy and
computational efficiency of the indigenously developed code is also
clearly established using relevant validations and case studies.
FEATURES Provides step-by-step procedure for the implementation of
particle swarm optimization (PSO) based algorithm in the context of
multilayered radar absorbing structures (RAS) design Helps to
understand the EM design, analysis and optimization of multilayered
RAS Describes the theoretical background required for the analysis
of multilayered RAS Illustrates in detail the theoretical
formulation supported by intuitive ray diagrams and comprehensive
flowcharts to implement the algorithm with ease Includes elaborate
validations and case studies This book will serve as a valuable
resource for students, researchers, scientists, and engineers
involved in the electromagnetic design and development of
multi-layered radar absorbing structures.
This book presents a novel methodology for the computation of RCS
of metallic structures using a parallelized version of NEC in
conjunction with a finite element preprocessor which has been
strategically incorporated for simplifying geometry modelling
catering to NEC guidelines. It includes a thorough overview of the
theoretical background of NEC including all relevant aspects of
formulation and modelling. The revised methodology including all
the required steps and details is discussed elaborately along with
case studies and validations. This book will serve as a valuable
resource for students, researchers, scientists, and engineers
working in the field of RCS predictions and measurements.
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