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.
A two-dimensional (2D) dynamically reconfigurable metasurface aperture is presented to perform frequency selective through wall imaging (TWI) with an unknown structure of the wall. Generally, in TWI, the medium properties and thickness of the wall need to be known in advance, which is not always possible. Moreover, compensating for these effects can significantly increase the computational complexity. We propose a two-stage method that leverages the concept of a dynamically reconfigurable metasurface antenna (DMA) in a narrow frequency band in which the effects of the wall are minimum to perform TWI. First, two simple probe antennas are used to evaluate the reflection response of the wall by means of a simple backscatter measurement. Based on these characteristics, a narrow band frequency selective window is identified. Second, a DMA consisting of an array of tunable metamaterial elements is used for TWI in the identified frequency selective window. The DMA aperture enables the scene information to be sampled through a set of spatio-temporally varying quasi-random modes using a single-channel transmit and receive architecture. This physical-layer compression scheme can significantly simplify the data acquisition while the quasi-random sampling of the scene information eliminates the need for conventional raster-scan based modalities.INDEX TERMS Reconfigurable antennas, microwave imaging, through wall imaging, metasurface.
A compressive sensingbased approach for millimeter-wave imaging compatible with Fourier-based image reconstruction techniques. In Proceedings of the 23rd International Radar Symposium, IRS 2022 (pp. 87-91). (International Radar Symposium Proceedings).
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 complexvalued 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.
Millimetre-Wave (mmWave) based on frequency-modulated continuous-wave (FMCW) technique as applied to synthetic aperture radar (SAR) imaging has many potential applications related to security, automotive, mapping, medical, and surveillance. In this paper, a three-dimensional (3D) SAR imaging system at K band frequencies is presented and its effective performance is verified with the reconstructed images from a set of uniformly sampled data from the scene. The collected backscattered signals are measured over a 2D plane, in both horizontal and vertical directions, using a Nearfield Systems Inc. (NSI) measurement platform. This paper presents the complete details about the measurement steps along with the signal processing SAR technique for 3D image reconstruction. The presented work demonstrates the reconstruction of image for two different targets. The performance of the work is also verified with the 3D reconstructed image of one target in a concealed scenario.
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