Phaseless Electromagnetic Inversion for Grain Bin Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current electromagnetic inversion systems for grain bin monitoring require calibration and prior information, which can be challenging to obtain, especially when access to the container is limited, and they rely on phase information that can be corrupted by measurement errors.
Innovation Solution
A phaseless, parametric inversion method that uses uncalibrated magnitude data from an antenna array to estimate the contents of a container without phase comparisons, providing information on moisture content and grain volume by comparing modeled and measured signal magnitudes, thus eliminating the need for calibration targets and prior information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electromagnetic inversion systems use calibration targets and prior information, then measurement precision is improved, but device complexity and ease of operation deteriorate due to required calibration procedures and access requirements
Solution Approach 1:
The system performs self-calibration by using the container walls themselves as reference structures. The inversion algorithm automatically identifies and uses the known geometric properties of the container walls to calibrate the electromagnetic data, eliminating the need for external calibration targets or prior information about container characteristics.
Solution Approach 2:
The container walls serve as an intermediary reference structure between the antenna array and the unknown contents. By using the walls' known geometric properties as a mediator, the system can calibrate the electromagnetic measurements without requiring direct access to calibration targets or prior information about the imaging region.
2Measurement precision
If phase information is used in electromagnetic inversion, then measurement precision may be improved, but reliability deteriorates due to phase corruption from measurement errors
Solution Approach 1:
The system extracts and uses only the magnitude information from the electromagnetic signals, deliberately discarding the phase information. This extraction of the useful magnitude component while removing the problematic phase component resolves the contradiction by maintaining measurement precision through magnitude accuracy while eliminating reliability issues associated with phase corruption.
3Measurement precision
If calibration procedures are implemented, then measurement precision is improved, but loss of time increases due to calibration requirements
Solution Approach 1:
The system performs calibration actions implicitly during the normal imaging process rather than as a separate preliminary step. The self-calibration is integrated into the inversion algorithm itself, so that the same computational process that generates the image also performs the calibration, eliminating the need for separate calibration procedures and associated time losses.
4Measurement precision
If known calibration targets are introduced into the imaging region, then measurement precision is improved, but device complexity and ease of operation worsen due to access requirements
Solution Approach 1:
The system uses the container walls themselves as the calibration reference, eliminating the need for external calibration targets. The walls' inherent geometric properties serve as the calibration reference, so no additional calibration objects need to be introduced into the imaging region, reducing system complexity and eliminating access requirements.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate determination of grain moisture content and volume within a container without the need for calibration or phase information, improving the efficiency and reliability of grain bin monitoring by using uncalibrated data to generate high-quality images.
Implementation Method 1
uses radio-frequency signals, a series of antennas placed inside of a grain bin
Implementation Method 2
The electrical permittivity may be used to determine the moisture contents of the grain stored in a bin
Data Source
AI summary
A method for electromagnetic imaging of containers receives uncalibrated first data corresponding to signals of a first plurality of different frequencies associated with an antenna array residing in a container having contents. The method estimates of a second data based on a computer model and simulation of signals of a second plurality of different frequencies associated with the antenna array, the second plurality of different frequencies including a subset of the first plurality of different frequencies. The method compares magnitudes, without corresponding phase comparisons, of the first and second data at each frequency of the second plurality of different frequencies. The method updates the second data based on the comparing. The method provides information about the contents within the container based on the updated second data.