Array Layout Optimization in a 24-Element 38-GHz Active Incoherent Millimeter-Wave Imaging System

Abstract

Active incoherent millimeter-wave (AIM) imaging is a recently developed technique that has been shown to generate fast millimeter-wave imaging using sparse apertures and Fourier domain sampling. In these systems, spatial frequency sampling is determined by cross-correlation between antenna pairs, making array geometry an important aspect that dictates the field of view (FOV) and image quality. This work investigates the impact of array redundancy and spatial sampling diversity on AIM image reconstruction performance. We present a comparative study of three receive array configurations, including one simple circular design and two arrays obtained through optimization strategies designed to maximize unique spatial samples while preserving system resolution and FOV. Performance is evaluated using the image-domain metrics of structural similarity index (SSIM) and peak sidelobe level (PSL), enabling a quantitative assessment of reconstruction fidelity and artifact suppression. We perform experimental validation using a 38-GHz AIM imaging system, implementing a 24-element receive array within a 48-position reconfigurable aperture. Results demonstrate that optimized array configurations improve spatial sampling efficiency and yield measurable gains in reconstruction quality compared to a conventional circular array, highlighting the importance of array design for AIM imaging systems.

Figures (11)

• Figure 1: Possible spaces numbered for the multi-objective optimization. This is a representation of the physical possible spaces in the bracket that was designed for the imaging system. Circles with diameter equal to waveguide size are added around each possible placement, indicating the minimum inter-element distance constraint between elements constraint in the receive array.
• Figure 2: Array formations in $\lambda/2$ grid, (a) circular array, (b) random-search array, (c) MO Array.
• Figure 3: Sampling function of each array shown in figure \ref{['SimAr']}.
• Figure 4: Theoretical PSF of each case in figure \ref{['SimAr']}. It can be seen the PSF changes depending on the array formation. The PSF can be used to characterize the imaging system in term of unambiguous FOV, resolution and quality of image reconstruction of the imager.
• Figure 5: Sidelobe level (SLL) versus declination plot. Using as reference the center pixel and strongest point of the PSF we take 360$\degree$ of angle around. This plot shows the comparison of the three array PSF SLL at every angle in increments of 1$\degree$. The behavior shown is expected and it can be appreciated as the SLL in the MO and random-search case is lower, it lowers it by a value of 7 dB(random-search case) 9 dB (MO case).