Abstract
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.
Highlights
As a new paradigm first proposed in the mid-1990s, computational imaging transfers the burden of image formation from solely optical physics to both front-end measurements and post-detection processing, providing a desired imaging capability with reduced requirements in size, weight, power or cost [1]
Our work shows that the mutual coherences are dependent on the spectral coherences of the sources, while the imaging mutual coherences are dependent on the spectral coherences of the sources, while the imaging scenario scenario acts equivalently as a low-pass filter in the k-space
The spectra of the measurement modes at the imaging range of 1 m, 2 m and 4 m are shown in (b), (c) and (d) respectively. It shows that the components with high spatial frequency are substantially attenuated with the increasing of the imaging range, which approximately conforms to Equation (10). This result validates that the imaging scenario acts equivalently as a spectral low-pass filter of which the cut-off frequency decreases with the increasing of the imaging range
Summary
As a new paradigm first proposed in the mid-1990s, computational imaging transfers the burden of image formation from solely optical physics to both front-end measurements and post-detection processing, providing a desired imaging capability with reduced requirements in size, weight, power or cost [1]. Various antenna structures have been proposed for MCI by intuitively randomizing resonant cavity structures or element resonant properties, such as mode-mixing cavity [10], printed aperiodic cavity with irises distributed in a Fibonacci pattern [11], disordered cavity with dynamically tuneable impedance source [12] and printed metasurface with subwavelength metallic cylinders in the cavity [13] These antennas have shown that the mutual coherences in the measurement matrix can be effectively reduced by introducing disorder and randomness in the antenna structures. These heuristic works do not give an explicit formulation of the coherences’ formation, which burdens further theoretical analysis and optimization.
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