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Characterization of Dielectric Materials by Sparse Signal Processing with Iterative Dictionary Updates

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Document pages: 6 pages

Abstract: Estimating parameters and properties of various materials without causingdamage to the material under test (MUT) is important in many applications.Thus, in this letter, we address this by wireless sensing. Here, the accuracyof the estimation depends on the accurate estimation of the properties of thereflected signal from the MUT (e.g., number of reflections, their amplitudesand time delays). For a layered MUT, there are multiple reflections and, due tothe limited bandwidth at the receiver, these reflections superimpose eachother. Since the number of reflections coming from the MUT is limited, wepropose sparse signal processing (SSP) to decompose the reflected signal. InSSP, a so called dictionary is required to obtain a sparse representation ofthe signal. Here, instead of a fixed dictionary, a dictionary update techniqueis proposed to improve the estimation of the reflected signal. To validate theproposed method, a vector network analyzer (VNA) based measurement setup isused. It turns out that the estimated dielectric constants are in closeagreement with the dielectric constants of the MUTs reported in literature.Further, the proposed approach outperforms the state-of-the-art model-basedcurve-fitting approach in thickness estimation.

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