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

TL;DR

This paper introduces an iterative dictionary update method within sparse signal processing to accurately characterize dielectric materials via wireless sensing, outperforming traditional curve-fitting techniques in thickness estimation.

Contribution

It proposes a novel dictionary update technique for sparse signal processing to improve dielectric property estimation of layered materials using wireless sensing.

Findings

Estimated dielectric constants closely match literature values.

Outperforms existing curve-fitting methods in thickness estimation.

Validated with VNA measurements on various MUTs.

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 this by wireless sensing. Here, the accuracy 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 each other. Since the number of reflections coming from the MUT is limited, we propose 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, a 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 are in close agreement with the dielectric constants of the MUTs reported in literature. Further, the proposed approach outperforms the state-of-the-art model-based curve-fitting approach in thickness estimation.