Imaging and Calibration of Electromagnetic Inversion Data With a Single Data Set

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.