Parametric Modeling of Radiation Patterns and Scattering Parameters of Antennas

Mutonkole, Ngoy; Samuel, Elizabeth Rita; Villiers, Dirk I. L. de; Dhaene, Tom

doi:10.1109/tap.2016.2521883

Cited by 17 publications

(13 citation statements)

References 20 publications

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“…The aim of the parametric CBFP method [6] is to approximate the radiation pattern at an arbitrary point x * in the This is the author's version of an article that has been published in this journal. Changes were made to this version by the publisher prior to publication.…”

Section: Parameterized Cbfpsmentioning

confidence: 99%

“…A singular value decomposition (SVD) of F M is then carried out, from which the first N R (N R N ) left-singular vectors of F M are retained, constituting a (possibly) reduced set of CBFPs, denoted by U R ∈ C Np×N R . The expansion coefficients corresponding to each CBFP (herein called the self-expansion coefficients) are the given by [6] …”

Section: Parameterized Cbfpsmentioning

confidence: 99%

“…Recently, a parametric modeling technique that yields accurate full radiation patterns, as a function of the antenna's geometric variables, has been proposed [6]. This method makes use of the characteristic basis function pattern (CBFP) method [7]- [9], where the expansion coefficients are parameterized with respect to the antenna's geometric variables.…”

Section: Introductionmentioning

confidence: 99%

“…This method makes use of the characteristic basis function pattern (CBFP) method [7]- [9], where the expansion coefficients are parameterized with respect to the antenna's geometric variables. The techniques in [1]- [6] make use of a pre-allocated grid, typically through a latin hypercube sampling (LHS) algorithm [10], of EM data from which models are extracted. Such a pre-allocation scheme may result in over/under-sampling of the design space, resulting in either inaccurate models or in an unnecessarily lengthy model building process.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed technique addresses the over/under-sampling issues elucidated earlier and can be executed without a priori knowledge of the antenna's behavior over a given design space. This paper represents an improvement on the approaches in [6], [11] and is, to our knowledge, the first adaptive sampling method for parameterized full radiation patterns. The proposed algorithm is easily extended to include S-parameter variations, thereby providing a full characterization of an antenna using a minimum number of EM simulations.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate Adaptive Sampling of Parameterized Antenna Responses

Mutonkole

Villiers

2017

IEEE Trans. Antennas Propagat.

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Abstract-We present a robust method to adaptively construct parameterized models of the full radiation patterns of antennas and the associated S-parameters. The method sequentially selects points (geometric parameters of the antenna and frequency) such that an accurate model is obtained over a constrained multivariate parameter space. The algorithm consists of a balance between exploration and exploitation of the parameter space, resulting in a near optimal coverage of the design space, with some emphasis being placed in regions of the parameter space where the patterns or S-parameters vary rapidly. In addition, the technique is equipped with a measure of absolute error control. The proposed method is validated through pertinent numerical examples.

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Section: Parameterized Cbfpsmentioning

confidence: 99%

Section: Parameterized Cbfpsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multivariate Adaptive Sampling of Parameterized Antenna Responses

Mutonkole

Villiers

2017

IEEE Trans. Antennas Propagat.

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Efficient optimization of wideband reflector feed antennas for optimal receiving sensitivity

Mokhupuki

Villiers

2020

Int J Numerical Modelling

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Wideband reflector antenna feeds are a geometrically complex class of antennas, requiring a variety of design parameters to describe their form. This casts the optimization of such structures as a high-dimensionality problem. The antenna responses are also difficult to predict and must be determined via computationally expensive full-wave electromagnetic simulations. This paper proposes a surrogate-based optimization approach for the design of wideband feed antennas. Beginning with high-fidelity goal function data from a global search space, the framework nominates a number of local search regions where separate, formal, multifidelity optimizations are performed. The formulated goal function maximizes the receiving sensitivity, while ensuring that the reflection coefficient response satisfies a predefined criterion. An approximate, fast-to-evaluate coarse model of the goal function, based on a series of surrogate models estimating the feed and reflector antenna responses, is formulated.Most of the optimization burden is shifted to the coarse model, where highfidelity samples are adaptively added to the regions of the design space where the coarse model performs well or where the sampling is sparse. As a design example, the optimization of a quad-ridge flared horn feed antenna, on a Square Kilometre Array reflector system, is presented to highlight the efficacy of the method. K E Y W O R D S characteristic basis function pattern method, quad-ridge flared horn antenna, reflector antennas, surrogate-based optimization | INTRODUCTIONIn the field of radio astronomy, there exists a need for high fidelity, wideband, single-pixel reflector feed antennas with an operational bandwidth exceeding one octave. A feed meeting such requirements would allow new-generation radio telescope projects, such as the Square Kilometre Array (SKA), 1 to reduce their total number of feeds per reflector-due to the wide bandwidth of each feed-and still maintain their predefined performance specifications. The primary benefit of this is a reduction of system costs; furthermore, the simultaneous coverage of wider bandwidths would also enable the pursuit of more ambitious science goals.

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Modeling of Electroencephalogram Parameters

Kharchenko,

Kovacheva

2024

Lecture Notes in Networks and Systems

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Parametric Modeling of Radiation Patterns and Scattering Parameters of Antennas

Cited by 17 publications

References 20 publications

Multivariate Adaptive Sampling of Parameterized Antenna Responses

Multivariate Adaptive Sampling of Parameterized Antenna Responses

Efficient optimization of wideband reflector feed antennas for optimal receiving sensitivity

Modeling of Electroencephalogram Parameters

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