2023Unpublished venueRequires access

Estimating Relative Permittivity of Material Filled in Waveguide Using Compressed Sensing

Masahiro Takishima, Naoki Honma, Kentaro Murata

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Abstract

This paper proposes a compressed-sensing based method to estimate the relative permittivity of a material filling up a waveguide. Two monopole antennas are placed at both ends of the waveguide, and the S-parameters between the antennas are measured. Relative permittivity is estimated by applying compressed sensing to the frequency characteristics of the S21, where two-dimensional bins for distance and relative permittivity are defined to find unknown parameters. The simulation result showed that this technique can estimate the relative permittivity within a 7.3% error.

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What this paper is about

This paper proposes a compressed-sensing based method to estimate the relative permittivity of a material filling up a waveguide. Two monopole antennas are placed at both ends of the waveguide, and the S-parameters between the antennas are measured. Relative permittivity is estimated by applying compressed sensing to the frequency characteristics of the S21, where two-dimensional bins for distance and relative permittivity are defined to find unknown parameters. The simulation result showed that this technique can estimate the relative permittivity within a 7.3% error.

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Available abstract

This paper proposes a compressed-sensing based method to estimate the relative permittivity of a material filling up a waveguide. Two monopole antennas are placed at both ends of the waveguide, and the S-parameters between the antennas are measured. Relative permittivity is estimated by applying compressed sensing to the frequency characteristics of the S21, where two-dimensional bins for distance and relative permittivity are defined to find unknown parameters. The simulation result showed that this technique can estimate the relative permittivity within a 7.3% error.

Key concepts: Relative permittivity, Permittivity, Waveguide, Approximation error, Acoustics, Materials science, Compressed sensing, Computer science

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