Emerging dynamic metasurface antennas (DMAs) technology promises to achieve future wireless communications by reducing hardware costs, physical size and power consumption. Especially in the field of radar imaging, they can be used as an effective alternative platform for modern computational imaging; because they can simplify the physical hardware and increase the data acquisition rate. Fourier transform (FT)-based scene image reconstruction techniques are known as cost-effective computing solutions for the imaging system processing unit. However, due to the physical layer compression in DMAs and the fact that they do not produce uniform radiation patterns, the information provided by them is not compatible with Fourier-based techniques and cannot be applied directly. In this paper, we first introduce a 3D near-field bistatic imaging approach using two one-dimensional (1D) DMAs as a panel-to-panel model in a Mills Cross structure. Then, based on the introduced mathematical model, we derive a Fourier-based algorithm for the image reconstruction problem. The proposed algorithm consists of five main steps: pre-processing (to transfer data provided by the transmitter and receiver DMAs to a set of equivalent spatial measurements), applying FTs to the transferred signal, filtering in the Fourier domain, a simplified interpolation, and applying a 3D inverse FT to retrieve scene information. The results of numerical simulations confirm the satisfactory performance of the proposed approach.

Electromagnetic waves at millimetre-wave (mmW) frequencies have found applications in a variety of imaging systems, from security screening to defence and automotive radars, with the research and development of mmW imaging systems gaining interest in recent years. Despite their significant advantages, mmW imaging systems suffer from poor resolution as compared to higher frequency reconstructions, such as optical images. To improve the resolution of mmW images, various super-resolution (SR) techniques have been introduced. One such technique is the use of machine learning algorithms in the signal processing layer of the imaging system without altering any of the system’s parameters. This paper focuses on the use of a convolutional neural network (CNN) architecture to achieve SR when applied to three-dimensional mmW input images. To exploit the phase information content of the input images along with the magnitude, a complex-valued CNN is designed that can accommodate complex-valued data. To simplify the learning process, the resolution difference between the input and output images is divided into smaller parts by using sub-networks in the CNN architecture. The trained model is tested on simulated as well as experimental targets. The average mean-squared-error score and structural similarity index obtained on a test dataset of 460 samples are 0.0127 and 0.9225, respectively. It can be inferred that the model has the capability to improve the resolution of input mmW images to a high degree of fidelity, hence paving the way for an end-to-end SR imaging system.

Near-field (NF) microwave and millimeter-wave (mm-wave) imaging, extending into the terahertz (THz) frequency range, has seen remarkable advancements across diverse applications, particularly security screening. These technologies benefit from the unique properties of microwave, mm-wave, and THz (MMT) spectra, such as penetration, nonionizing radiation, material sensitivity and the capability to operate in all weather conditions. This article provides an overview of the evolution and current state of NF radar imaging, emphasizing the critical role of signal processing in overcoming challenges related to hardware complexity, long acquisition time, and image reconstruction quality. Advanced signal processing techniques—including Fourier-based algorithms, sparse imaging, low-rank matrix recovery, and deep learning—are highlighted for their contributions to enhancing image resolution and processing efficiency. The article also discusses recent innovations in antenna technologies, aperture configurations, and scanning methods that have significantly improved NF radar imaging capabilities. Future research directions are suggested to further advance the field, highlighting the importance of continued exploration and innovation in NF MMT imaging.

In this work, we propose and analyze a thermally tunable metasurface based on indium antimonide (InSb), designed to operate in the terahertz (THz) frequency range. The metasurface exhibits dual functionalities: single-band perfect absorption and efficient polarization conversion, enabled by the temperature-dependent permittivity of InSb. At approximately 280 K, InSb transitions into a metallic state, enabling the metasurface to achieve near-unity absorptance (100%) at 0.408 THz under normal incidence, independent of polarization. Conversely, when InSb behaves as a dielectric at 200 K, the metasurface operates as an efficient polarization converter. By exploiting structural anisotropy, it achieves a polarization conversion ratio exceeding 85% over the frequency range from 0.56 to 0.93 THz, while maintaining stable performance for incident angles up to 45°. Parametric analyses show that the resonance frequency and absorption intensity can be effectively tuned by varying the InSb square size and the silica (SiO₂) layer thickness, achieving maximum absorptance at a SiO₂ thickness of 16 μm. The proposed tunable metasurface offers significant potential for applications in THz sensing, imaging, filtering, and wavefront engineering.

Achieving high imaging resolution in conventional monostatic radar imaging with mechanical scanning requires excessive acquisition time. Although real aperture radar systems might not suffer from such a limitation in acquisition time, they may still face challenges in achieving high imaging resolution, especially in near-field (NF) scenarios, due to diffraction-limited performance. Even with sophisticated electronic scanning techniques, increasing the aperture size to improve resolution can lead to complex hardware setups and may not always be feasible in certain practical scenarios. Although multistatic systems provide flexibility in increasing the effective aperture virtually, they introduce challenges related to the number of antennas and channels, which can still pose fundamental hardware limitations. This is because array antennas can be expensive, exhibit a poor form-factor, and typically consume a lot of power since they require complex control circuitry and many radio frequency components. An alternative solution that has been proposed in recent years is the compression of the physical layer using metasurface transducers. In this paper, by leveraging dynamic metasurface antennas, with multiple tuning states called masks, in a bistatic structure, a novel approach for NF radar imaging using the Kirchhoff migration principle is presented. By expanding the compressed measured signal using a data transformation from the mask-frequency domain to the spatial-frequency domain, the scene's spatial content is decoded. The Kirchhoff integral is then developed based on the introduced special imaging structure to retrieve the three-dimensional spatial information of the target. By using numerical simulations, a comprehensive analysis of the characteristics of masks and their behavior under different conditions is provided. Also, the performance of the image reconstruction algorithm is evaluated in terms of visual quality and computing time using both central processing unit and graphics processing unit. The results of computer simulations confirm the high reliability of the proposed approach in various cases.

In this paper, to accelerate data acquisition and image reconstruction procedures in a multistatic short-range microwave imaging scenario, an orthogonal coding approach with Fourier domain processing is presented. First, a special two-dimensional (2D) multiple-input multiple-output (MIMO) structure is introduced to fully electronically synthesize the 2D aperture. Then, the model of the transmitted and received signals by a MIMO stepped-frequency-modulated radar is presented, with special considerations about orthogonal, balanced and optimal sequences. On the receiver side, the backscatter frequency response extraction process is formulated with the aim of obtaining individual information of all channels. Finally, based on the introduced model, a fast Fourier-based algorithm with reduced dimensions, named MIMO coded generalized reduced dimension Fourier (CGRDF), is mathematically derived. It includes extracting phase and amplitude compensators with the aim of mapping 4D to 2D spatial data, transferring the backscatter transfer function from the spatial domain to the wavenumber domain, extracting the smoothing filter, compensating the curvature of the wavefront of all scatterers, extracting the reflectivity function and an additional range compensator. The results of numerical simulations show the satisfactory and reliable performance of the proposed approach in terms of the information retrieval process and processing speed.