Publications

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

Thanthrige, U.S.K.P.M. and Barowski, J. and Rolfes, I. and Erni, D. and Kaiser, T. and Sezgin, A.

IEEE SENSORS LETTERS
Volume: 4 Pages:
DOI: 10.1109/LSENS.2020.3019924
Published: 2020

Abstract
Estimating parameters and properties of various materials without causing damage to the material under test (MUT) is important in many applications. Thus, in this letter, we address MUT's parameter estimation by wireless sensing. Here, the precision of the estimation depends on the accurate estimation of the properties of the reflected signal from the MUT (e.g., number of reflections, their amplitudes, and time delays). For a layered MUT, there are multiple reflections, and due to the limited bandwidth at the receiver, these reflections superimpose with each other. Since the number of reflections coming from the MUT is limited, we utilize sparse signal processing (SSP) to decompose the reflected signal. In SSP, a so called dictionary is required to obtain a sparse representation of the signal. Here, instead of a fixed dictionary, an iterative dictionary-update technique is proposed to improve the estimation of the reflected signal. To validate the proposed method, a vector network analyzer (VNA)-based measurement setup is used. It turns out that the estimated dielectric constants of the MUTs are in close agreement with those reported in literature. Further, the proposed approach outperforms the state-of-the-art model-based curve-fitting approach in thickness estimation. © 2017 IEEE.

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