Minkowski Sum Method for Planar Arrays Sensitivity Analysis With Uncertain-But-Bounded Excitation Tolerances

Tenuti, L.; Anselmi, Nicola; Rocca, Paolo; Salucci, Marco; Massa, Andrea

doi:10.1109/tap.2016.2627548

Cited by 46 publications

(36 citation statements)

References 22 publications

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“…It is worth mentioning that beside rectangular intervals (Cartesian IA), circular intervals can also be used for complex parameters; the difference between the results of these two kinds of intervals and the 'wrapping effect' [19] should be researched in the future.…”

Section: Discussionmentioning

confidence: 99%

“…We determine the bounds of T . We assume that the interval T 1 does not include zero, 0 ∉ T 1 , and have (see (19)…”

Section: Novel Ia-based Approachmentioning

confidence: 99%

“…Anselmi predicted the impact of the manufacturing tolerance of linear antenna arrays excitation amplitudes and phase errors on radiation pattern based on interval arithmetic computing [17,18]. Tenuti compared the Minkowski sum method with the interval-based method for planar arrays [19]. Circular interval arithmetic was used to analyse the power pattern considering mutual coupling in linear arrays [20].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of power pattern in CLAS with the material thickness and properties error through interval arithmetic

Pedrycz¹,

Song

2017

IET Microwaves, Antennas & Propagation

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A novel analytical approach based on interval arithmetic is proposed to investigate the effect of material thickness and properties errors on the average power pattern in the conformal load‐bearing antenna structures (CLAS) for both local and global errors conditions. The uncertainties of the thickness or properties error of CLAS composite material are modelled as interval‐valued parameters. The dominant expressions between the thickness or properties error interval and the power pattern interval are derived by interval arithmetic along with some main electromagnetic characteristics (side‐lobe level, peak power, and half‐power beamwidth) expressed as intervals can be produced through interval analysis (IA). Some numerical examples are reported to validate the proposed approach and to show its reliability and efficiency when considering different thickness and property errors. The obtained results show that the proposed IA‐based approach offers tangible advantages and effectiveness against some traditional statistical techniques (such as the Monte Carlo method).

show abstract

Section: Discussionmentioning

confidence: 99%

“…We determine the bounds of T . We assume that the interval T 1 does not include zero, 0 ∉ T 1 , and have (see (19)…”

Section: Novel Ia-based Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of power pattern in CLAS with the material thickness and properties error through interval arithmetic

Pedrycz¹,

Song

2017

IET Microwaves, Antennas & Propagation

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show abstract

“…In outer working environment, reflector antennas are easily susceptible to surface distortion resulting from gravitational, thermal, dynamic exterior forces and random errors [2]. With the famous Ruze formula [3] and other investigated expressions [4][5][6][7][8][9][10], which approximately relate surface distortion to radiation properties, the electromagnetic performance can be evaluated through surface root-mean-square (rms) error considering the systematic errors and random errors. However, in the recent demands of high frequency and large diameter [11], the conventional antenna design concept based on simply reducing surface rms error has encountered several obstacles [2].…”

Section: Introductionmentioning

confidence: 99%

“…For the former one, there are some scientific literatures addressing approximation methods to accelerate the electromagnetic re-analysis. The most common treatment for approximating distorted pattern is attaching the surface distortion into far-field radiation integral as additional phase error, which is usually employed in the error analysis for reflector antennas [3][4][5][6][7] with deterministic, probabilistic, and interval analysis [7][8][9][10]. Another treatment is to expand the exponential phase error term into first-order [20][21][22], second-order [23,24], and piecewise linear fitting expansions [25].…”

Section: Introductionmentioning

confidence: 99%

Second‐order sensitivity analysis of electromagnetic performance with respect to surface nodal displacements for reflector antennas and its benefits in integrated structural–electromagnetic optimisation

Zhang

Deng

et al. 2017

IET Microwaves, Antennas & Propagation

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Integrated structural–electromagnetic optimisation design is an interesting area in the field of antenna design. Due to the urgent requirements on second‐order sensitivity analysis to benefit the integrated iterative procedure, based on the authors’ previous work, an analytic second‐order partial derivative formula of electromagnetic performance – directivity with respect to surface nodal displacements for reflector antennas is deduced. In this formula, the electromagnetic performance is obtained by superposition of radiation integrals on each small triangular element. Then, a perturbation of surface nodal displacement is introduced to re‐express the distorted radiation integral. By partially differentiating the distorted phase error in radiation integral with respect to nodal perturbation over the corresponding triangular element twice, the second‐order partial derivative is derived. Two reflectors – an axis‐symmetrical one and an offset one, are illustrated in simulation to show the second‐order partial derivatives in contour plots. A comparative simulation is performed to illustrate its benefits in integrated structural–electromagnetic optimisation.

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Statistical analysis of phased arrays with random excitation errors using a unified neural network architecture

Zhao

2023

Int J Numerical Modelling

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Radiation patterns of a phased array tend to be affected by random excitation errors. In this paper, a unified neural network (UNN) architecture is presented to study the statistical characteristics of radiation patterns. Benefited from the inherent symmetry of the array manifolds and efficient data preprocessing, the UNN architectures are almost identical for planar arrays consisting of 4–10 000 elements except for a slight difference existed in the output layer. Therefore, this merit avoids repetitive selections of training hyperparameters and network architectures. This method can efficiently treat the problems with multiple observation points in order to obtain statistical radiation patterns, and an analytic or closed‐form solution is not required. Moreover, it can combine with the method of moments (MoM), which takes account of the element pattern and mutual coupling effects. The trained UNN models can predict the probability density function (PDF) of radiation characteristics at any spatial location. Numerical results show that the UNN and Monte Carlo (MC) results are comparable in accuracy.

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Minkowski Sum Method for Planar Arrays Sensitivity Analysis With Uncertain-But-Bounded Excitation Tolerances

Cited by 46 publications

References 22 publications

Analysis of power pattern in CLAS with the material thickness and properties error through interval arithmetic

Analysis of power pattern in CLAS with the material thickness and properties error through interval arithmetic

Second‐order sensitivity analysis of electromagnetic performance with respect to surface nodal displacements for reflector antennas and its benefits in integrated structural–electromagnetic optimisation

Statistical analysis of phased arrays with random excitation errors using a unified neural network architecture

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