Near-field propagation of cyclostationary stochastic electromagnetic fields

Russer, Johannes A.; Russer, P.; Konovalyuk, Maxim; Gorbunova, Anastasia; Baev, Andrey; Kuznetsov, Yu.V.

doi:10.1109/iceaa.2015.7297360

Cited by 22 publications

(6 citation statements)

References 11 publications

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“…The Green function and MoM methods also have been extended for application to cyclostationary stochastic fields [ 58 – 60 ]. Areas of application are the modelling of the EM interference radiated by digital circuitry inside the system and also into the environment, where the period of the cyclostationary EMI is given by the clock frequency of the digital circuits.…”

Section: From Deterministic To Statistical Electromagnetic Theorymentioning

confidence: 99%

Stochastic electromagnetic field propagation— measurement and modelling

Gradoni

Russer

Baharuddin

et al. 2018

Phil. Trans. R. Soc. A.

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This paper reviews recent progress in the measurement and modelling of stochastic electromagnetic fields, focusing on propagation approaches based on Wigner functions and the method of moments technique. The respective propagation methods are exemplified by application to measurements of electromagnetic emissions from a stirred, cavity-backed aperture. We discuss early elements of statistical electromagnetics in Heaviside's papers, driven mainly by an analogy of electromagnetic wave propagation with heat transfer. These ideas include concepts of momentum and directionality in the realm of propagation through confined media with irregular boundaries. We then review and extend concepts using Wigner functions to propagate the statistical properties of electromagnetic fields. We discuss in particular how to include polarization in this formalism leading to a Wigner tensor formulation and a relation to an averaged Poynting vector.This article is part of the theme issue ‘Celebrating 125 years of Oliver Heaviside's ‘Electromagnetic Theory’’.

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Section: From Deterministic To Statistical Electromagnetic Theorymentioning

confidence: 99%

Stochastic electromagnetic field propagation— measurement and modelling

Gradoni

Russer

Baharuddin

et al. 2018

Phil. Trans. R. Soc. A.

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“…Different from this, in the general case a complete experimental near-field characteriza-tion providing all necessary information for the computation of the energy density distribution in complex environments requires sampling of the EM field with at least two field probes in order to measure also the cross correlations of the field components sensed at the different field points [12], [13], [24]- [26]. The two-point correlation field measurement and analysis methods were discussed for cyclostationary EM fields in [27], [28]. Figure 1 shows the schematic of a two-point scanner used for measuring and recording the near-field correlation of stochastic EM fields [12].…”

Section: Introductionmentioning

confidence: 99%

Cyclostationary Characterization of Radiated Emissions in Digital Electronic Devices

Kuznetsov

Baev

Konovalyuk

et al. 2020

IEEE Electromagn. Compat. Mag.

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Coupling and Emissions Under Cyclostationary Conditions For convenience, engineers often make the assumption that the statistical behaviour of signals remains constant in time (timeinvariant). However, as the complexity of modern systems increases and the simultaneous presence of several clocked sources of EMI, this assumption is not always valid. Cyclostationary signals have hidden periodicities in the way they transport electromagnetic energy. Cyclostationarity was first studied in connection with mechanical signals. Studying them in connection with EMC is used in identifying patterns of emission from specific areas of PCBs and therefore can assist in taking appropriate remedial actions. The paper by Kuznetsov et al presents an analysis of electrical signals through a process of statistical cyclic averaging to characterize the emission properties for EMC analysis and design. Experimental results are also presented in support of the theoretical analysis. In future issues of the EMC Magazine we will cover topics such as the Electrical Behavior of CFCs, Stochastic EMC Applications, and Lightning Characterization.

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“…Modeling of cyclostationary stochastic fields has been addressed in previous studies. [20][21][22] Stochastic EM fields are also considered in literature in the context of fields inside reverberation chambers. 23 Furthermore, variations in the geometry or in material parameters of the structure supporting the wave propagation can be treated with stochastic techniques which use polynomial chaos expansion, stochastic collocation techniques, [24][25][26] or stochastic differential equations.…”

Section: Introductionmentioning

confidence: 99%

“…However, EMI in this case is likely to exhibit cyclostationary rather than stationary properties, ie, mean values of the field and correlation terms will be periodic in time. Modeling of cyclostationary stochastic fields has been addressed in previous studies …”

Section: Introductionmentioning

confidence: 99%

Principal component analysis for efficient characterization of stochastic electromagnetic fields

Haider

Russer

2017

Int J Numerical Modelling

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The propagation of stochastic electromagnetic fields can be accurately modeled using the auto-and cross-correlation spectra of the field components. In this work, we introduce the framework of principal component analysis for reducing the computational cost of handling stochastic electromagnetic fields described by correlation matrices. We consider noisy electromagnetic fields originating from stationary random processes with Gaussian probability distribution. The amount of data obtained by 2-dimensional near-field measurements and by determining the autoand cross-correlation information of electromagnetic interference can become burdensome for further processing even for problems of moderate size. For obtaining the correlation data, 2 measurement probes have to scan a defined grid of measurement points. For each pair of points, the spatial correlations need to be calculated, and hence, the data obtained scales quadratically with the number of sampling points.To reduce the amount of data, we project the given data obtained by measurement or simulation onto directions of maximum variation, the so called principal components, and keep only those principal components which contribute most to the total variance. In this way, the memory demand for storage and further computation of stochastic electromagnetic fields can be reduced significantly. KEYWORDS electromagnetic interference, noisy electromagnetic fields, principal component analysis, stochastic electromagnetic fields Int J Numer Model. 2018;31:e2246.wileyonlinelibrary.com/journal/jnm

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Near-field propagation of cyclostationary stochastic electromagnetic fields

Cited by 22 publications

References 11 publications

Stochastic electromagnetic field propagation— measurement and modelling

Stochastic electromagnetic field propagation— measurement and modelling

Cyclostationary Characterization of Radiated Emissions in Digital Electronic Devices

Principal component analysis for efficient characterization of stochastic electromagnetic fields

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