49 research outputs found
Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool
Accelerated by the increasing attention drawn by 5G, 6G, and Internet of
Things applications, communication and sensing technologies have rapidly
evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years.
Enabled by significant advancements in electromagnetic (EM) hardware, mmWave
and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz,
respectively, can be employed for a host of applications. The main feature of
THz systems is high-bandwidth transmission, enabling ultra-high-resolution
imaging and high-throughput communications; however, challenges in both the
hardware and algorithmic arenas remain for the ubiquitous adoption of THz
technology. Spectra comprising mmWave and THz frequencies are well-suited for
synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide
spectrum of tasks like material characterization and nondestructive testing
(NDT). This article provides a tutorial review of systems and algorithms for
THz SAR in the near-field with an emphasis on emerging algorithms that combine
signal processing and machine learning techniques. As part of this study, an
overview of classical and data-driven THz SAR algorithms is provided, focusing
on object detection for security applications and SAR image super-resolution.
We also discuss relevant issues, challenges, and future research directions for
emerging algorithms and THz SAR, including standardization of system and
algorithm benchmarking, adoption of state-of-the-art deep learning techniques,
signal processing-optimized machine learning, and hybrid data-driven signal
processing algorithms...Comment: Submitted to Proceedings of IEE
Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems
Increasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300
GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including
security sensing, industrial packaging, medical imaging, and non-destructive
testing. Traditional methods for perception and imaging are challenged by novel
data-driven algorithms that offer improved resolution, localization, and
detection rates. Over the past decade, deep learning technology has garnered
substantial popularity, particularly in perception and computer vision
applications. Whereas conventional signal processing techniques are more easily
generalized to various applications, hybrid approaches where signal processing
and learning-based algorithms are interleaved pose a promising compromise
between performance and generalizability. Furthermore, such hybrid algorithms
improve model training by leveraging the known characteristics of radio
frequency (RF) waveforms, thus yielding more efficiently trained deep learning
algorithms and offering higher performance than conventional methods. This
dissertation introduces novel hybrid-learning algorithms for improved mmWave
imaging systems applicable to a host of problems in perception and sensing.
Various problem spaces are explored, including static and dynamic gesture
classification; precise hand localization for human computer interaction;
high-resolution near-field mmWave imaging using forward synthetic aperture
radar (SAR); SAR under irregular scanning geometries; mmWave image
super-resolution using deep neural network (DNN) and Vision Transformer (ViT)
architectures; and data-level multiband radar fusion using a novel
hybrid-learning architecture. Furthermore, we introduce several novel
approaches for deep learning model training and dataset synthesis.Comment: PhD Dissertation Submitted to UTD ECE Departmen
Microwave Sensing and Imaging
In recent years, microwave sensing and imaging have acquired an ever-growing importance in several applicative fields, such as non-destructive evaluations in industry and civil engineering, subsurface prospection, security, and biomedical imaging. Indeed, microwave techniques allow, in principle, for information to be obtained directly regarding the physical parameters of the inspected targets (dielectric properties, shape, etc.) by using safe electromagnetic radiations and cost-effective systems. Consequently, a great deal of research activity has recently been devoted to the development of efficient/reliable measurement systems, which are effective data processing algorithms that can be used to solve the underlying electromagnetic inverse scattering problem, and efficient forward solvers to model electromagnetic interactions. Within this framework, this Special Issue aims to provide some insights into recent microwave sensing and imaging systems and techniques
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MIMO Coded Generalized Reduced Dimension Fourier Algorithm for 3-D Microwave Imaging
10.13039/501100000275-Leverhulme Trust under Research Leadership Award (Grant Number: RL-2019-019);
10.13039/501100007914-Brunel University London under Research Development Fund (Grant Number: LBG194);
10.13039/501100007914-2022/2023 Brunel Research Initiative and Enterprise Fund (Grant Number: 12455)
Three-Dimensional Microwave Imaging for Concealed Weapon Detection Using Range Stacking Technique
Three-dimensional (3D) microwave imaging has been proven to be well suited for concealed weapon detection application. For the 3D image reconstruction under two-dimensional (2D) planar aperture condition, most of current imaging algorithms focus on decomposing the 3D free space Green function by exploiting the stationary phase and, consequently, the accuracy of the final imagery is obtained at a sacrifice of computational complexity due to the need of interpolation. In this paper, from an alternative viewpoint, we propose a novel interpolation-free imaging algorithm based on wavefront reconstruction theory. The algorithm is an extension of the 2D range stacking algorithm (RSA) with the advantages of low computational cost and high precision. The algorithm uses different reference signal spectrums at different range bins and then forms the target functions at desired range bin by a concise coherent summation. Several practical issues such as the propagation loss compensation, wavefront reconstruction, and aliasing mitigating are also considered. The sampling criterion and the achievable resolutions for the proposed algorithm are also derived. Finally, the proposed method is validated through extensive computer simulations and real-field experiments. The results show that accurate 3D image can be generated at a very high speed by utilizing the proposed algorithm
Compressive Sensing for Microwave and Millimeter-Wave Array Imaging
PhDCompressive Sensing (CS) is a recently proposed signal processing technique that has
already found many applications in microwave and millimeter-wave imaging. CS theory
guarantees that sparse or compressible signals can be recovered from far fewer measure-
ments than those were traditionally thought necessary. This property coincides with the
goal of personnel surveillance imaging whose priority is to reduce the scanning time as
much as possible. Therefore, this thesis investigates the implementation of CS techniques
in personnel surveillance imaging systems with different array configurations.
The first key contribution is the comparative study of CS methods in a switched array
imaging system. Specific attention has been paid to situations where the array element
spacing does not satisfy the Nyquist criterion due to physical limitations. CS methods are
divided into the Fourier transform based CS (FT-CS) method that relies on conventional
FT and the direct CS (D-CS) method that directly utilizes classic CS formulations. The
performance of the two CS methods is compared with the conventional FT method in
terms of resolution, computational complexity, robustness to noise and under-sampling.
Particularly, the resolving power of the two CS methods is studied under various cir-
cumstances. Both numerical and experimental results demonstrate the superiority of CS
methods. The FT-CS and D-CS methods are complementary techniques that can be
used together for optimized efficiency and image reconstruction.
The second contribution is a novel 3-D compressive phased array imaging algorithm
based on a more general forward model that takes antenna factors into consideration.
Imaging results in both range and cross-range dimensions show better performance than
the conventional FT method. Furthermore, suggestions on how to design the sensing con-
figurations for better CS reconstruction results are provided based on coherence analysis.
This work further considers the near-field imaging with a near-field focusing technique
integrated into the CS framework. Simulation results show better robustness against
noise and interfering targets from the background.
The third contribution presents the effects of array configurations on the performance of
the D-CS method. Compressive MIMO array imaging is first derived and demonstrated
with a cross-shaped MIMO array. The switched array, MIMO array and phased array are
then investigated together under the compressive imaging framework. All three methods
have similar resolution due to the same effective aperture. As an alternative scheme for
the switched array, the MIMO array is able to achieve comparable performance with far
fewer antenna elements. While all three array configurations are capable of imaging with
sub-Nyquist element spacing, the phased array is more sensitive to this element spacing
factor. Nevertheless, the phased array configuration achieves the best robustness against
noise at the cost of higher computational complexity.
The final contribution is the design of a novel low-cost beam-steering imaging system
using a flat Luneburg lens. The idea is to use a switched array at the focal plane of
the Luneburg lens to control the beam-steering. By sequentially exciting each element,
the lens forms directive beams to scan the region of interest. The adoption of CS for
image reconstruction enables high resolution and also data under-sampling. Numerical
simulations based on mechanically scanned data are conducted to verify the proposed
imaging system.China Scholarship Council
Engineering and Physical Sciences
Research Council (EPSRC)
funding (EP/I034548/1)
1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface
A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Antennas and Propagation
This Special Issue gathers topics of utmost interest in the field of antennas and propagation, such as: new directions and challenges in antenna design and propagation; innovative antenna technologies for space applications; metamaterial, metasurface and other periodic structures; antennas for 5G; electromagnetic field measurements and remote sensing applications
Antenna Designs for 5G/IoT and Space Applications
This book is intended to shed some light on recent advances in antenna design for these new emerging applications and identify further research areas in this exciting field of communications technologies. Considering the specificity of the operational environment, e.g., huge distance, moving support (satellite), huge temperature drift, small dimension with respect to the distance, etc, antennas, are the fundamental device allowing to maintain a constant interoperability between ground station and satellite, or different satellites. High gain, stable (in temperature, and time) performances, long lifecycle are some of the requirements that necessitates special attention with respect to standard designs. The chapters of this book discuss various aspects of the above-mentioned list presenting the view of the authors. Some of the contributors are working strictly in the field (space), so they have a very targeted view on the subjects, while others with a more academic background, proposes futuristic solutions. We hope that interested reader, will find a fertile source of information, that combined with their interest/background will allow efficiently exploiting the combination of these two perspectives