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In recent years, computational imaging, which encodes scene information into a set of measurements, has become a research focus in the field of microwave imaging. As with other typical inverse problems, the key challenge is to reduce the mutual coherences in the measurement matrix which is composed of measurement modes. Since the modes are synthesized by antennas, there is a great deal of interest in the antenna optimization for the reduction. The mechanism underlying the generation of the coherences is critical for the optimization; however, relevant research is still inadequate. In this paper, we try to address the research gap by relating the coherences to the antennas’ equivalent radiation sources via spectral Green’s dyad. We demonstrate that the coherences in the measurement matrix are dependent on the spatial spectral coherences of the sources, while in this relationship the imaging scenario acts as a spectral low-pass filter. Increasing the imaging range narrows the spectral constraint, which eventually increases the coherences in the measurement matrix. Full-wave electromagnetic simulations are performed for validation. We hope that our work provides a possible direction for the antenna optimization in microwave computational imaging (MCI) applications and motivates further research in this field.

On the Coherence Relationship between Measurement Matrices and Equivalent Radiation Sources in Microwave Computational Imaging Applications