Characterization of Layered Dielectric Materials Using Ultra-Wideband FMCW-Radar Measurements

Jebramcik, Jochen; Barowski, Jan; Rolfes, Ilona

doi:10.23919/apmc.2018.8617322

Cited by 13 publications

(5 citation statements)

References 9 publications

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“…The aforementioned considerations do not consider any multiple reflections within the medium; however, in many practical cases, e.g. material characterization, this is not a valid assumption [7], [8]. The following is a straighforward extension of recent works [7], [61].…”

Section: B Dielectric Slab Including Internal Reflectionsmentioning

confidence: 94%

“…Frequency independent data such as geometric path lengths are stored during this phase. Next, for each frequency of interest, we loop over all launched rays and calculate the transmission from the source to the surface facet, then calculate the corresponding frequency dependent electric and magnetic current densities, and finally compute the observed field at the receiver according to (8) and (9). Eventually, we calculate S 11 as proposed in [64] for each frequency.…”

Section: Methodsmentioning

confidence: 99%

“…In many practical cases dielectric scatterers are present in the simulation domain. [8], [18], [54] Therefore, the PO has been extended towards penetrable bodies in [34], [36], [57]. Different formulations for the electric and magnetic current densities J pen and M pen are available in the previously mentioned literature.…”

Section: A Physical Opticsmentioning

confidence: 99%

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Considering Nonsurface Scattering in Physical Optics Approximations

Garten

Statz

Gerling

et al. 2021

IEEE Trans. Antennas Propagat.

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This work addresses the issue of volume scattering effects within the context of the physical optics (PO) approach. This decreases the modeling and computational effort to simulate scattering from complex material compositions. It is shown that there is a natural progression from the classical PO for perfect electric conductors over the PO for dielectric scatterers towards the proposed formulation. Four specializations of the general algorithm are presented to emphasize the versatility of this approach. Details regarding the implementation of the proposed examples are described. Results for each of the special cases are shown and compared to commercially available full-wave solvers of CST and FEKO.

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Section: B Dielectric Slab Including Internal Reflectionsmentioning

confidence: 94%

Section: Methodsmentioning

confidence: 99%

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Considering Nonsurface Scattering in Physical Optics Approximations

Garten

Statz

Gerling

et al. 2021

IEEE Trans. Antennas Propagat.

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“…where 12 is the intrinsic reflection coefficient of the initial reflection and is given by 12 = ( √ ϵ r,1 − √ ϵ r,2 )/( √ ϵ r,1 + √ ϵ r,2 ), and γ represents the propagation constant defined as γ = 2π f √ ϵ r,2 /c. ϵ r,1 is the dielectric constant of the medium, ϵ r,2 = ϵ r (1 − j tan δ) is the complex dielectric constant of the slab, c is the speed of light and f is the frequency.…”

Section: B Analytic Model Of a Dielectric Slabmentioning

confidence: 99%

“…Frequency-modulated continuous wave (FMCW) radars have been utilized in a range of applications, including high accuracy distance measurements [1], [2], [3], vital signs monitoring [4], [5], [6], and hand gesture recognition [7], [8]. They have also been shown in the literature to be effective tools for material characterization, as they offer a low-cost, compact, and accurate solution for characterizing dielectric materials [9], [10], [11], [12]. Thus, they can be considered as a practical and industrially viable alternative to vector network analyzers (VNAs).…”

mentioning

confidence: 99%

Deep Learning-Based Material Characterization Using FMCW Radar With Open-Set Recognition Technique

Abouzaid

Jaeschke²,

Kueppers³

et al. 2023

IEEE Trans. Microwave Theory Techn.

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This article proposes a low-cost and practical alternative to vector network analyzers (VNAs) for characterizing dielectric materials using a calibrated frequency-modulated continuous wave (FMCW) radar measurement setup and a machine learning (ML) model. The calibrated FMCW radar measurement setup has the ability to accurately measure the S-parameters of dielectric materials. In addition, an ML model is developed to extract material parameters such as thickness, dielectric constant, and loss tangent with high accuracy. K -means clustering was additionally applied to significantly reduce the complexity of the neural network (NN). Additionally, a state-of-the-art openset recognition (OSR) technique was adopted to simultaneously classify known classes and reject unknown classes. The developed model uses a modified version of the class anchor clustering (CAC) distance-based loss, which outperforms the conventional cross-entropy loss. The proposed model was evaluated on several dielectric materials and compared to reference measurements using a VNA and curve fitting. The results indicate that the proposed model is accurate and robust, and that the calibrated radar sensor provides a practical and cost-effective alternative to VNAs in characterizing dielectric materials, as long as the material parameters are within the defined limits.

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