Planar stochastic sources localization algorithm in EMC problems

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

doi:10.1109/iceaa.2013.6632275

Cited by 14 publications

(4 citation statements)

References 4 publications

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“…The periodic sample mean function μ x (t) of the measured cyclostationary component x(t) of the signal (3) is given by (16) If the evaluated sample mean function μ x (t) coincides with periodic expectation function m X (t), the stochastic process X(t) possesses the first order cycloergodic property. It means that statistical cyclic averaging < > of the random signal x(t) gives the same result as probabilistic ensemble averaging E{ } of the stochastic process X(t).…”

Section: P Td T P Tmentioning

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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Section: P Td T P Tmentioning

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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“…Expanding the correlation dyadic, periodic in time with T 0 , into a Fourier series yields (6) are referred to as the cyclic time-domain autocorrelation dyadics and the frequencies nf 0 = nω 0 /2π are the so-called cycle frequencies [15], [19]. The cyclic correlation spectrum (CCS) of the electric field Γ E,n (x a , x b , ω) is given by…”

Section: Spectral Representation Of the Cs Em Fieldmentioning

confidence: 99%

Near-field correlation measurement and evaluation of stationary and cyclostationary stochastic electromagnetic fields

Russer

Haider

Baharuddin

et al. 2016

2016 46th European Microwave Conference (EuMC)

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We present methods for measurement and evaluation of stationary and cyclostationary stochastic electromagnetic fields. The radiated electromagnetic interference (EMI) of electronic circuitry is recorded by two-point measurements of the tangential electric or magnetic field components and by evaluating the field autocorrelation functions and for each pair of field sampling points also the cross correlation functions. In case of digital circuitry clocked by a single clock pulse, the generated EMI is a cyclostationary process where the expectation values of the EMI are periodically time dependent according to the clock frequency and which have to be considered in modeling the EMI.

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“…The function describes the scalar field in time domain. The Fourier transform for a deterministic field would be (18) A correlation function can be defined for the field function by (19) More specifically, this function is termed the autocorrelation function of the field for and cross-correlation function for [12], [14]. Now, let us look at the Fourier transform of a sample of the noise field, which is of finite length in time .…”

Section: Scalar Stochastic Fieldsmentioning

confidence: 99%

Modeling of Noisy EM Field Propagation Using Correlation Information

Russer

2015

IEEE Trans. Microwave Theory Techn.

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In this paper, an efficient method for the numerical simulation of near-and far-field propagation of stochastic electromagnetic (EM) fields is presented. The method is based on the transformation of field correlation dyadics using Green's functions or the field transfer functions computed for deterministic fields. The method accounts for arbitrary correlations between the noise radiation sources and allows to compute the spatial distribution of the spectral energy density of noisy electromagnetic sources. The introduced methodology can be combined with available electromagnetic modeling tools. It is shown that the method of moments can be applied to solve noisy electromagnetic field problems by network methods applying correlation matrix techniques. Examples demonstrating the strong influence of the correlation between the sources on the spatial distribution of the radiated noise field are presented.Index Terms-Electromagnetic interference, near-field scanning, noisy electromagnetic fields, stochastic electromagnetic fields. 0018-9480

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Planar stochastic sources localization algorithm in EMC problems

Cited by 14 publications

References 4 publications

Cyclostationary Characterization of Radiated Emissions in Digital Electronic Devices

Cyclostationary Characterization of Radiated Emissions in Digital Electronic Devices

Near-field correlation measurement and evaluation of stationary and cyclostationary stochastic electromagnetic fields

Modeling of Noisy EM Field Propagation Using Correlation Information

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