This study introduces an innovative optimization method to identify the optimal configuration of a sparse symmetric 2D array for applications in security, particularly multistatic imaging. Utilizing genetic algorithms (GAs) in a sophisticated optimization process, the research focuses on achieving the most favorable antenna distribution while mitigating the common issue of secondary lobes in sparse arrays. The main objective is to determine the ideal configuration from specific design parameters, including hardware specifications such as number of radiating elements, minimum spacing, operating frequency range, and image separation distance. The study employed a cost function based on the the point spread function (PSF), the system response to a point source, with the goal of minimizing the secondary lobe levels and maximizing their separation from the main lobe. Advanced simulation algorithms based on physical optics (PO) were used to validate the presented methodology and results.

Recent advances in millimeter-wave multiple-input multiple-output (MIMO) radar architectures have enabled security screening systems capable of detecting concealed threats, including nonmetallic hazardous objects. However, these systems often face challenges related to the distribution of high-frequency signals and bulky external antennas, increasing the complexity and cost of the system. In this work, we present a D-band MIMO radar where high-frequency signals are generated locally at each element of the array. The proposed design leverages commercially available monolithic microwave integrated circuits (MMICs) with on-chip antennas, ensuring a compact and scalable solution. By integrating a horizontal 1-D MIMO array with a mechanical aperture, our system achieves 3-D imaging with subcentimeter cross-range resolution at distances below 3 m.

This study introduces an innovative optimization method to identify the optimal configuration of a sparse symmetric 2D array for applications in security, particularly multistatic imaging. Utilizing Genetic Algorithms (GA) in a sophisticated optimization process, the research focuses on achieving the most favorable antenna distribution while mitigating the common issue of secondary lobes in sparse arrays. The main objective is to determine the ideal configuration from specific design parameters, including hardware specifications such as number of radiating elements, minimum spacing, operating frequency range, and image separation distance. The study employs a cost function based on the the point spread function (PSF), the system response to a point source, with the goal of minimizing the secondary lobe levels and maximizing their separation from the main lobe. Advanced simulation algorithms based on Physical Optics (PO) are used to validate the presented methodology and results.

The IFMIF-DONES (International Fusion Materials Irradiation Facility – DEMO Oriented Neutron Source) is designed to reproduce neutron irradiation conditions in a fusion reactor. In this setup, a 5 MW deuteron beam collides with a liquid lithium target flowing at 15 m/s and maintained at 300°C. The resulting deuteron-lithium stripping reactions produce high flux neutrons with an energy spectrum comparable to that of future fusion reactors, thereby enabling testing of fusion-relevant materials, under controlled and homogenous conditions. A key component of this facility is the target assembly, where the ion beam interacts with the free surface of the liquid lithium target, which must maintain a thickness of 25 ± 1 mm to safely absorb the beam energy and minimize the neutron flux losses. This region of the facility is subjected to intense radiation, high temperatures, and lithium evaporation, making real-time diagnostics a challenging task. To address this hazard conditions, a radar-based system operating in the millimeter-wave (mmWave) range has been proposed. This system is able to continuously monitor the lithium thickness and to detect flow instabilities, interfacing with the Machine Protection System (MPS) to enable fast beam shutdown if the critical thickness is compromised. For initial technology feasibility tests, a commercial radar kit operating in the same frequency range planned for IFMIF-DONES has been acquired. Preliminary experiments include static-target tests that evaluate distance accuracy and material reflection characteristics using metallic and dielectric plates, followed by dynamic-target tests using water and liquid metals to simulate possible disturbances in the lithium flow. These experiments will validate the technology viability proposed as a solution for the fast MPS system and for quick monitoring of the Li surface homogeneity before implementing it in a real liquid lithium loop of IFMIF-DONES facility.

Representative labeled radar data are difficult to acquire across the target and scene variability needed for automatic detection. Physics-based simulation offers an alternative, but its value depends on whether detectors trained on simulated responses generalize beyond training targets. This work examines that question using acquisition-aware electromagnetic simulation of a D-band multiple-input multiple-output (MIMO) radar. Complex multistatic responses are generated before image formation, and simulated and measured data undergo the same coherent reconstruction and detector-input preparation. No measured target responses are used during development. Seven convolutional neural network (CNN) detectors are compared, with YOLOX-Tiny selected based on detection performance and inference cost. Across seven geometry-disjoint folds, YOLOX-Tiny achieves mean $F_2=0.89$ and ${\mathrm{mAP}}_{0.5}=0.88$ for single-object scenes, and $F_2=0.82$ and ${\mathrm{mAP}}_{0.5}=0.79$ for multi-object scenes. Generalization is further examined on 14 independent jointly simulated multi-object scenes and measurements from the reference D-band radar and two additional systems. The measurements provide qualitative evidence of transfer and reveal replica-related false detections and local class ambiguities in high-resolution responses. Lower-resolution measurements retain correctly detected targets alongside additional false detections assigned to other classes. These findings highlight the need for more representative synthetic radar responses and for relating image detections to physical targets.