Abstract
Electromagnetic Imaging (EMI) systems use a large number of co-resident antennas usually connected to a Vector Network Analyzer via a switch. A numerical model is used to model the physical electromagnetic problem and an inversion algorithm is used to invert the collected data to produce an image of the target. However, before the computer model can be used, the raw VNA measurements must be calibrated to bridge the computational model and the true system physics. Traditional calibration approaches usually require two data sets: a data set measured from a known target for calibration purposes and a data set measured for the unknown target. In this paper, we introduce a new calibration method to calibrate and image using a single S-parameter measurement of the unknown target only. We apply this method to EMI inside of grain bins. This proposed calibration workflow: (1) estimates the bulk contents of the grain bin using a parametric inversion and (2) uses the bulk results to subsequently estimate per-channel calibration coefficients for both the transmit and receive paths to each antenna. The novel calibration procedure is demonstrated via both synthetic and experimental results, showing that single-data set calibration can provide similar quality results as traditional two-data set calibration.
Highlights
M ost of the world’s crops are harvested stored for later processing and consumption
Signals are generated and S-parameters are measured using Vector Network Analyzer (VNA), delivered through a switch network and coaxial cables to 24 antennas installed inside a bin
Once Hsct,cal is produced by the single-shot calibration process, we can proceed to apply an inversion algorithm to detect hot-spots that may be present in the grain
Summary
M ost of the world’s crops are harvested stored for later processing and consumption. Grain-bin EMI allows the reconstruction of 3D maps of the permittivity inside the bin which can be used to monitor the grain for safe storage conditions over time Results for such systems have been previously reported in [2], [3], [4], [5]. Signals are generated and S-parameters are measured using Vector Network Analyzer (VNA), delivered through a switch network and coaxial cables to 24 antennas installed inside a bin. These S-parameter measurements at a set of specific frequencies are calibrated and used in an inversion algorithm that provides quantitative 3D images of the complex electric permittivity of the content of the bin. This two-data set process is the same for many other types of inversion algorithm and applications (e.g. [6], [7], [8], [9], [10], [11] and others)
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