Electromagnetic Imaging System Calibration With 2-Port Error Models

Abstract

Calibration is essential in electromagnetic imaging for converting the raw measurements to a usable form for the imaging algorithm. The complexity of the calibration technique can range between a simple comparison of the raw measurement to those of a known calibration target, to a comprehensive simulation of the entire imaging chamber. This work introduces a novel approach to calibration that models the antennas and field propagation as 2-port networks (rather than scalars or a comprehensive model), for which common network theory and de-embedding techniques can be applied. The accuracy of the proposed 2-port method is experimentally tested against the scalar calibration technique on a 2D imaging system. The use of both metallic and dielectric calibration objects is tested, and the inversion performance is compared for the calibration techniques. For the experimental system tested herein, the use of a 2-port model for each transmitter/receive antenna pair moderately improved both calibration accuracy and image quality compared to a simple scalar calibration coefficient, for the cost of measuring a minimum of 2 known calibration targets.

Keywords

• Inverse problems
• calibration
• electromagnetic tomography
• microwave imaging
• modeling