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A Novel Statistical Model for the Electromagnetic Coupling to Electronics inside Enclosures

Figure 8: Information capacity of MIMO array in RC

Editor’s Note:  The paper on which this article is based was originally presented at the 2019 IEEE International Symposium on Electromagnetic Compatibility & Signal/Power Integrity (EMC, SI & PI), where it received recognition as the Best Symposium Paper. It is reprinted here with the gracious permission of the IEEE. Copyright 2020 IEEE.

Introduction

The directed energy (DE) high power radio-frequency (HPRF) weapons have become an increasing threat to various mission-critical and safety-critical electronic systems and facilities. The intentional electromagnetic interference (IEMI) from the HPRF sources may introduce noise or signals into circuits and electronics of the target system, thus degrade, disable, or damage the system. To rapidly assess the vulnerability and susceptibility of electronics due to the HPRF attack, it is essential to develop a deep-domain understanding of the physics of HPRF-to-target-to-electronics coupling.

As recognized by many scientists and engineers in the IEMI research community, the physics of HPRF-to-target-to-electronics interaction is extremely complex [1]–[4]. A generic problem statement is shown in Figure 1. Most of electronics are hosted inside protective metallic enclosures, metal castings, and computer boxes. The HPRF energy first needs to couple into the target enclosure/casing through open apertures or seams, and then interact with the sensitive electronics.

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Figure 1: EM coupling into the computer model

In the high-frequency regime, the complex boundary of the enclosure can lead to high modal density and high modal overlap. Wave solutions inside these enclosures show strong fluctuations that are extremely sensitive to the geometry of the enclosure, the location of internal sensors and electronics, and the operating frequency. Minor differences in the system configuration can result in significantly different EM field distributions inside the enclosure. Research regarding HPRF effects on missiles has shown large variations between different serial numbers due to assembly methods, cable routing, and component variations [5].

Evidently, because of the increasing complexity of electronic systems and continually evolving DE HPRF waveforms, it is expensive and impractical to perform experimental tests for all possible HPRF effects. The objective of this work is to investigate physics-oriented mathematical and statistical models, which discover and replicate the fundamental physics of HPRF coupling on electronic systems. The main contributions are described as follows.

1) Wave-chaotic coupling: To characterize the multipath, ray-chaotic propagation inside the enclosure, a stochastic Green’s function (SGF) representation formula is proposed. The SGF represents the fundamental solution of wave equations in the complex wave-chaotic media (domains exhibiting ray-chaotic dynamics). The derivation of the scalar SGF is presented in the recent work [6]–[9], leveraging the physics of wave-chaotic dynamics [10], [11] and the mathematics of random matrix theory (RMT) [12], [13]. In this paper, we propose the vector dyadic SGF and the statistical representation formula. The work rigorously characterizes both variations and correlations of EM fields inside complex enclosures.

2) System-specific aspects: Moreover, we recognize that the EM coupling inside complex systems may involve mixed chaotic and regular wave dynamics. The system-specific coupling ranges from aperture/slot geometries, to site-specific short-orbits, to the configuration of electronic components. Those non-chaotic dynamical features result in deviations from universality and ergodicity in fully wave chaos. To answer this challenge, we propose a hybrid deterministic and stochastic formulation incorporating component-specific characteristics and investigate an in-situ SGF representation formula addressing the site-specific short-orbits.

Comparing to existing literature [14]–[21], the work rigorously integrates the system-specific coupling and the wavechaotic coupling, and seamlessly incorporating universal statistical properties and deterministic coupling characteristics within a comprehensive framework. The developed predictive models are validated and verified in various experimental settings, including complex 3D metallic cavities and mode-stirred reverberation chambers.

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Methodology

Motivation for Stochastic Green’s Function

In the short-wavelength regime, the wave scattering process inside complex enclosures may exhibit chaotic ray dynamics [22]. EM fields show high variability and extreme sensitivity to small perturbations. Given the complexity of such environments, it is thus very desirable to have the statistical properties of EM fields within complex enclosures. Nevertheless, the standard statistical treatment of this problem depends on many parameters that are immensely complex [17].

It has been recognized that the ergodic modes in cavity environments lead to certain universal statistical properties of EM fields [23]–[25]. For instance, in overmoded reverberation chambers, the probabilistic EM fields present uniform phase distribution and Rayleigh distributed amplitude. The variance of the Rayleigh distribution is related to the quality factor of the chamber, instead of the exact shape of the enclosure or the location of the measurement. The homogeneous and structureless statistical behavior motivates the investigation of the stochastic Green’s function, which describes the generic statistical properties of wave dynamics inside the cavity rather  than detailed specifics.

Review of Green’s Function in Free Space

The Green’s function stands for the fundamental solution of a partial differential equation. In the electromagnetic theory, the Green’s function describes the physics of the wave propagation from the source point rʹ to the receiving point r. In the homogeneous, free-space scenario, we have the well-known 3D dyadic Green’s function as:

  (1)

where I is the identity tensor, and k is the wave number of the operating frequency.

The imaginary part of G0 can be expressed as the following plane wave integral:

  (2)

By applying the Sokhotski-Plemelj theorem, the real part, Re[G0 (r, rʹ; k)] can be expressed as:

  (3)

Where denotes the Cauchy principal value. Equations (2) and (3) give rise to the Kramers-Kronig relation.

Stochastic Green’s Function in Chaotic Media

The stochastic Green’s function represents the fundamental, probabilistic solution of the wave equation in chaotic environments. Consider the 2nd order vector wave equation inside a metallic cavity, the Green’s function in the dyadic form can be constructed from the eigenfunction expansion:

  (4)

Where indicates a tensor product. Ψi and ki are the ith eigenfunction and eigenvalue of the cavity.

1) Random wave model: Based on the Berry’s random plane wave hypothesis [26], eigenfunctions of the wave-chaotic cavity can be locally modeled as an isotropic, random superposition of plane waves. Let’s start by assuming that field and source points, rrʹ are close to each other and away from the cavity boundary, the eigenfunction Ψi can thereby be expressed by the ensemble:

 (5)

where the direction ên, amplitude αn, and phase βn are independent random variables. The r0 is the center of the plane wave expansion. Based on the central limit theorem, the summation over a large number N plane waves results in the Gaussian-distributed eigenfunction values. Equation (4) can be rewritten as:

  (6)

where the ωi is zero mean, unit width Gaussian random variable, and V is the enclosed volume of the cavity.

Comparing Eqs. (6) and (3), the sum over eigenmodes in GS (r, rʹ; k) approximates, on average, the integral in Eq. 3 but with a statistically fluctuating contribution coming from the denominator when eigenvalues kn ≈ k. We can rewrite the expression of Eq. (6) as:

  (7)

2) Random matrix theory: A key observation is that the statistical variation of SGF in Eq. (7) mainly depends on the sum of eigenmodes whose eigenvalues are close to k, and the distance between r and rʹ. It provides the inspiration for introducing a universal random quantity, gco, defined as:

  (8)

where ∆ is the mean-spacing between adjacent eigenvalues. For large metallic cavities, the mean-spacing is given as ∆ = 4π2/kV ) [20]. Moreover, to characterize the generic losses for realistic cavities (dielectric losses, ohmic losses, etc.), we introduce a dimensionless cavity loss-parameter, α = k2/ (∆Q), predicted from the Weyl law [27], where Q represents the average quality factor [19].

The eigenvalues kn obey locally repulsion laws, which are distributed according to the random matrix hypotheses of Wigner. The statistics of the normalized eigenvalue distributions (k2k2n) /∆ is predicted by RMT with the Gaussian Orthogonal Ensemble of random matrices [10], [12].

3) Universality in stochastic Green’s function: We notice that the gco is denoted as a universal statistical quantity, since its statistics do not depend on the explicit geometry of the enclosure or the location of the sources and receivers within the enclosure. Rather, they are determined by a few macroscopic parameters, including the operating frequency, the volume, and the loss-parameter.

It can be easily shown that the stochastic Green’s function preserves the translation invariance. The GS is a function of the difference r – rʹ and the statistical variable gco only. Moreover, based on the stochastic Green’s function, we can derive the spatial field-field correlation function. The result gives a universal two-point correlation function, which agrees with Berry’s random wave model conjecture.

4) The case of large separation: In the case of r and rʹ are well separated, we can use two random plane wave expansions to approximate locally Ψi (r, ki) and Ψi (rʹ, ki). The resulting Green’s function GS (r, rʹ; k) is derived as:

  (9)

where ωn, ωʹn are two independent Gaussian random variables with zero mean and unit variance, and

The statistical variation can then be denoted as a universal random quantity, guc, defined as:

  (10)

It is straightforward to prove that the mean value of Eq. (9) is zero. The result can be understood via reverberation chambers. The perfect mixing (well-stirred) over a number of cavity realizations leads to a zero mean complex field, when the measurement point is far away from the source. Due to page restrictions, the discussion of intermediate separation case will be deferred to the extended paper.

Statistical Representation Formula

The stochastic Green’s function characterizes the universal statistical behavior of the wave-chaotic propagation. As a direct application, one can use it to obtain the probabilistic values of induced EM fields due to a distributed current source. It gives the following statistical representation formula (SRF):

  (11)

The SRF also provides the impedance relation between the surface electric field and electric current. By making use of the vector-dyadic Green’s second identity, Equation 11 can be reduced a surface integral form:

  (12)

which maps the tangential component of magnetic field to the tangential component of electric field.

Consider a source and a receiver (denoted as components) located within a complex metallic enclosure and far from boundaries. We introduce trial and testing functions on the surfaces of the components. The discrete matrix form of SRF can be decomposed as:

  (13)

where Z01,2 are impedance matrices obtained by using freespace Green’s function, corresponding to the real part of the Green’s function in Eq. (7). The A01,2 are the outgoing plane wave representations of the source currents, where D01,2 = [A01,2] denote the incoming representations. The g∗∗ represent the Gaussian diffuse energy propagator, whose statistical values are obtained from Eqs. (8) and (10) with random matrix theory.

Integration with System-Specific Aspects

1) Component-specific features: Evidently, the HPRF coupling effects are not only determined by the cavity environment, but also by the electronics and antennas under test. To integrate the specific knowledge (types, geometries, materials, et al.) of the electronic components, we propose a hybrid deterministic and stochastic formulation.

In the proposed framework, electronic components (antennas, devices, apertures, etc.) in the computational domain are modeled by the volume-based finite element (FE) method. The statistical representation formula is placed on the exterior surfaces of those components, emulating the multipath, ray-chaotic propagation environments. The resulting system matrix equation can be written as:

  (14)

In Eq. (14), AFE represents the FE matrix, ZSBE is the boundary element (BE) discretization of the surface representation formula. The matrix C denotes the coupling between FE and BE matrices. The probabilistic solutions of EM and electrical quantities can be obtained by stochastic matrix collocation or Monte Carlo approaches. We notice that the matrix equation (14) only involves the unknowns on the surface and volume of the electronic components. Therefore, this is no need to discretize the cavity enclosure.

2) Site-specific short-orbits: One important factor that causes the deviation from chaotic universality is due to short-orbits, whose phase accumulation along the trajectories may not be large enough to be considered as random. Consider an antenna mounted on the wall of the cavity enclosure, as illustrated in Figure 2. There exists direct, short-orbit couplings between the antenna and the neighboring wall. These short ray trajectories should be considered as classical coherent rays, rather than chaotic ray trajectories which ergodically sample the enclosure. Clearly, the existence of short orbits makes nonuniversal contributions to the statistical EM field behavior.

Figure 2: Site-specific short-orbit correction

This challenge is addressed by studying an in-situ statistical representation formula, which integrates the site-specific, deterministic interactions with the universal statistical behavior of the cavity. In the computational model, we shall include the site-specific features, namely, the antenna and its neighboring cavity wall, as shown in Figure 2. The first step is to evaluate the non-isotropic plane wave representation on the boundary surface of the antenna, denoted by ÃPA, where the superscript indicates that it is a mapping from antenna surface current to plane wave spectrum. Taking the short-orbit coupling into consideration, ÃPA is calculated as:

  (15)

where Z0CA is the coupling impedance matrix from antenna to the surrounding cavity wall. The Z0CC is the free-space impedance matrix of the cavity wall. A0PA and A0PC are the free-space plane wave representations for surface currents on antenna and cavity wall, respectively.

Subsequently, the non-isotropic plane wave representation, ÃPA, is substituted in Eq. (13) to obtain the in-situ statistical representation formula. In practice, the size of the in-situ short-orbit region is pre-quantified through the calculation of the Ehrenfest time [28], [29], which is the time scale of the transition from classical to chaotic ray trajectories.

Experiments

Experimental Validation with 3D Cavity

We consider a complicated 3D aluminum cavity to validate the proposed work. The geometry and photograph of the experimental setup are illustrated in Figure 3(a)-(b). Two Xband waveguide (WG) antennas are mounted on the different locations of cavity walls as transmitter (Tx) and receiver (Rx). The cavity is significantly overmoded and EM fields exhibit wave chaotic fluctuations. A paddle-wheel mode stirrer is used to generate an ensemble of measurements. At each frequency, the mode stirrer is rotated 200 positions over 360 degrees.

Figure 3: Configuration of the 3D metallic enclosure

The volume of the cavity is 0.42m3 and the cavity loss-parameter α = 9.9516. Based on Eqs. (8) and (10), we generate the universal statistical quantities, gco and guc. The probability distribution function (PDF) of the real and imaginary parts of gco and guc obtained by computational predictions and measurement results are compared in Figure 4.

Figure 4: PDF of the universal random variables, gco and guc.

In the computational setup, we only need to include the site-specific features, namely, the WG antennas and their surrounding aluminum plates. The computational domains are illustrated in Figure 5(b) and Figure 6(a), where antennas are mounted on the adjacent walls and opposite walls in the experiments, respectively. The interaction with other parts of the 3D cavity is categorized statistically with the SGF on the exterior surface of the antennas. As a result, the solution of the reduced order, stochastic model can be obtained at the same cost as an antenna radiating in free space. The PDF of the antenna S-parameters is depicted in Figure 5(c) and Figure 6(b), where excellent agreements are observed.

Figure 5: Experimental validation of the stochastic wave model (frequency range: 9.62‑9.68GHz)

 

Figure 6: Experimental validation of the stochastic wave model (frequency range: 9.52-9.58GHz)

Experimental Validation with Reverberation Chamber

Next, we present the experimental validation with a reverberation chamber (RC) measurement. RC has been used as a standard testing facility for electromagnetic compatibility (EMC) measurements. Recent works have also demonstrated the use of RC as an efficient emulator for rich fading environments and over-the-air (OTA) testing of wireless devices [30]– [33]. It is particularly useful to analyze the correlation and capacity of multiple-input multiple-output (MIMO) systems in rich multipath environments.

Shown in Figure 7 is a reference setup presented in [30] for measuring a six-element monopole circular MIMO array. The chamber is of size 0.8m × 1.05× 1.5m, and equipped with two mechanical plate-shaped stirrers. The six-element monopole array is located on a rotatable platform (circular metal plate with radius 0.14m) and rotated inside the chamber (platform stirring). Three fixed wall antennas are used for polarization stirring. The measurements were done at 900MHz. The spacing between monopole antennas is 0.24λ.

Figure 7: Configuration of the reverberation chamber [30]

The computational model includes three wall antennas (Tx) and six-element monopole array on the metal plate (Rx). The spatial multipath propagation between them is characterized by the stochastic Green’s function representation formula. The outcome provides a probabilistic ensemble of S-parameter matrices describing the transmission performance. The random transfer (channel) matrix, H, from the Tx antennas to the Rx antennas can then be calculated as:

  (16)

where ZG and ZL are Tx and Rx load impedances.

The calculated ergodic channel capacity is compared to the measurement result in Figure 8, where a very good agreement is observed. Due to the close spacing between monopole antennas, the MIMO channel receives correlated wireless signals. In turn, it affects the channel capacity comparing to ideal Rayleigh fading channel. The results verify that the proposed work leads to a competent statistical model rigorously resolves the spatial correlation, propagating coherence, and antenna mutual coupling in the rich multipath environment.

Conclusion

The proposed work aims to establish a physics-oriented, reduced-order modeling capability, which predicts the statistical EM coupling rapidly while retaining the underlying first-principles analysis. The statistical model only needs the knowledge of site-specific features and deterministic coupling channels, e.g. the electronics, apertures, antennas, and coaxial cables. The interaction with other parts of the complex cavity is characterized statistically with a novel stochastic Green’s function representation formula. We can rapidly evaluate the susceptibility and/or vulnerability of the system to various HPRF waveforms and engagement angles. The advancements reduce the need for costly and time-consuming measurements.

Authors’ Note:  The work summarized in this paper was supported by U.S. NSF CAREER award, #1750839, and U.S. AFOSR/AFRL Center of Excellence on the Science of Electronics in Extreme Electromagnetic Environments, Grant FA9550-15-1-0171.

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