De-embedding electromagnetic imaging data on large storage bins

EP4548085A4Pending Publication Date: 2026-06-17GSI ELECTRONIQUE INC +1
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Patent Information

Authority / Receiving Office
EP · EP
Patent Type
Applications
Current Assignee / Owner
GSI ELECTRONIQUE INC
Filing Date
2023-05-18
Publication Date
2026-06-17

AI Technical Summary

Technical Problem

Electromagnetic imaging systems used for monitoring grain in large storage bins face challenges in de-embedding the effects of cables and switching circuits due to the need for full network parameters, which are not feasible with partial VNAs, especially since cables are cut to size during installation and cannot be measured beforehand.

Method used

A 2-port network de-embedding technique is employed using only a subset of S-parameters (S11 and S21) to model and remove the effects of the measurement system, estimating the remaining S-parameters and performing calibration, allowing for accurate imaging of grain properties without requiring full network characterizations or field calibration.

Benefits of technology

This approach improves the accuracy of electromagnetic imaging results while reducing the time and resources required for calibration, enabling effective monitoring of grain properties like moisture content in large bins.

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Abstract

In one embodiment, a system comprising: a container configured to store commodity; a measurement system comprising a vector network analyzer (VNA), a switch module, a plurality of cables, the container, and a plurality of antennas coupled to interior walls of the container, the switch module configured to switch signals transmitted to and received from the plurality of antennas via a plurality of channels, the VNA configured to measure scattering parameters (S-parameters) of all of the plurality of channels; a non-transitory computer readable medium comprising software; and a processor configured by the software to: de-embed a combined effect of the measurement system based on a 2-port network de-embedding technique using only a subset of the S-parameters; and provide an image of the commodity using an inversion algorithm based on input of a calibrated S-parameter after the de-embedding.
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