321 research outputs found
Innovative Adaptive Techniques for Multi Channel Spaceborne SAR Systems
Synthetic Aperture Radar (SAR) is a well-known technology which allows to coherently combine
multiple returns from (typically) ground-based targets from a moving radar mounted either on an airborne
or on a space-borne vehicle. The relative motion between the targets on ground and the platform
causes a Doppler effect, which is exploited to discriminate along-track positions of targets themselves.
In addition, as most of conventional radar, a pulsed wide-band waveform is transmitted periodically,
thus allowing even a radar discrimination capability in the range direction (i.e. in distance).
For side-looking acquisition geometries, the along-track and the range directions are almost
orthogonal, so that the two dimensional target discrimination capabiliy results in the possibility to
produce images of the illuminated area on ground. A side-looking geometry consists in the radar
antenna to be, either mechanically or electronically, oriented perpendicular to the observed area.
Nowadays technology allows discrimination capability (also referred to as resolution) in both alongtrack
and range directions in the order of few tenths of centimeters.
Since the SAR is a microwave active sensor, this technology assure the possibility to produce images
of the terrain independently of the sunlight illumination and/or weather conditions. This makes the SAR
a very useful instrument for monitoring and mapping both the natural and the artificial activities over
the Earth’s surface. Among all the limitations of a single-channel SAR system, this work focuses over some of them
which are briefly listed below:
a) the performance achievable in terms of resolution are usually paid in terms of system
complexity, dimension, mass and cost;
b) since the SAR is a coherent active sensor, it is vulnerable to both intentionally and unintentionally
radio-frequency interferences which might limit normal system operability;
c) since the Doppler effect it is used to discriminate targets (assumed to be stationary) on the
ground, this causes an intrinsic ambiguity in the interpretation of backscattered returns from
moving targets.
These drawbacks can be easily overcome by resorting to a Multi-cannel SAR (M-SAR) system
Innovative Adaptive Techniques for Multi Channel Spaceborne SAR Systems
Synthetic Aperture Radar (SAR) is a well-known technology which allows to coherently combine
multiple returns from (typically) ground-based targets from a moving radar mounted either on an airborne
or on a space-borne vehicle. The relative motion between the targets on ground and the platform
causes a Doppler effect, which is exploited to discriminate along-track positions of targets themselves.
In addition, as most of conventional radar, a pulsed wide-band waveform is transmitted periodically,
thus allowing even a radar discrimination capability in the range direction (i.e. in distance).
For side-looking acquisition geometries, the along-track and the range directions are almost
orthogonal, so that the two dimensional target discrimination capabiliy results in the possibility to
produce images of the illuminated area on ground. A side-looking geometry consists in the radar
antenna to be, either mechanically or electronically, oriented perpendicular to the observed area.
Nowadays technology allows discrimination capability (also referred to as resolution) in both alongtrack
and range directions in the order of few tenths of centimeters.
Since the SAR is a microwave active sensor, this technology assure the possibility to produce images
of the terrain independently of the sunlight illumination and/or weather conditions. This makes the SAR
a very useful instrument for monitoring and mapping both the natural and the artificial activities over
the Earth’s surface. Among all the limitations of a single-channel SAR system, this work focuses over some of them
which are briefly listed below:
a) the performance achievable in terms of resolution are usually paid in terms of system
complexity, dimension, mass and cost;
b) since the SAR is a coherent active sensor, it is vulnerable to both intentionally and unintentionally
radio-frequency interferences which might limit normal system operability;
c) since the Doppler effect it is used to discriminate targets (assumed to be stationary) on the
ground, this causes an intrinsic ambiguity in the interpretation of backscattered returns from
moving targets.
These drawbacks can be easily overcome by resorting to a Multi-cannel SAR (M-SAR) system
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
Holographic MIMO Communications: Theoretical Foundations, Enabling Technologies, and Future Directions
Future wireless systems are envisioned to create an endogenously
holography-capable, intelligent, and programmable radio propagation
environment, that will offer unprecedented capabilities for high spectral and
energy efficiency, low latency, and massive connectivity. A potential and
promising technology for supporting the expected extreme requirements of the
sixth-generation (6G) communication systems is the concept of the holographic
multiple-input multiple-output (HMIMO), which will actualize holographic radios
with reasonable power consumption and fabrication cost. The HMIMO is
facilitated by ultra-thin, extremely large, and nearly continuous surfaces that
incorporate reconfigurable and sub-wavelength-spaced antennas and/or
metamaterials. Such surfaces comprising dense electromagnetic (EM) excited
elements are capable of recording and manipulating impinging fields with utmost
flexibility and precision, as well as with reduced cost and power consumption,
thereby shaping arbitrary-intended EM waves with high energy efficiency. The
powerful EM processing capability of HMIMO opens up the possibility of wireless
communications of holographic imaging level, paving the way for signal
processing techniques realized in the EM-domain, possibly in conjunction with
their digital-domain counterparts. However, in spite of the significant
potential, the studies on HMIMO communications are still at an initial stage,
its fundamental limits remain to be unveiled, and a certain number of critical
technical challenges need to be addressed. In this survey, we present a
comprehensive overview of the latest advances in the HMIMO communications
paradigm, with a special focus on their physical aspects, their theoretical
foundations, as well as the enabling technologies for HMIMO systems. We also
compare the HMIMO with existing multi-antenna technologies, especially the
massive MIMO, present various...Comment: double column, 58 page
Spatially-Stationary Model for Holographic MIMO Small-Scale Fading
Imagine an array with a massive (possibly uncountably infinite) number of antennas in a compact space. We refer to a system of this sort as Holographic MIMO. Given the impressive properties of Massive MIMO, one might expect a holographic array to realize extreme spatial resolution, incredible energy efficiency, and unprecedented spectral efficiency. At present, however, its fundamental limits have not been conclusively established. A major challenge for the analysis and understanding of such a paradigm shift is the lack of mathematically tractable and numerically reproducible channel models that retain some semblance to the physical reality. Detailed physical models are, in general, too complex for tractable analysis. This paper aims to take a closer look at this interdisciplinary challenge. Particularly, we consider the small-scale fading in the far-field, and we model it as a zero-mean, spatially-stationary, and correlated Gaussian scalar random field. A physically-meaningful correlation is obtained by requiring that the random field be consistent with the scalar Helmholtz equation. This formulation leads directly to a rather simple and exact description of the three-dimensional small-scale fading as a Fourier plane-wave spectral representation. Suitably discretized, this yields a discrete representation for the field as a Fourier plane-wave series expansion, from which a computationally efficient way to generate samples of the small-scale fading over spatially-constrained compact spaces is developed. The connections with the conventional tools of linear systems theory and Fourier transform are thoroughly discussed
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)
Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays
Massive MIMO (multiple-input multiple-output) is no longer a "wild" or
"promising" concept for future cellular networks - in 2018 it became a reality.
Base stations (BSs) with 64 fully digital transceiver chains were commercially
deployed in several countries, the key ingredients of Massive MIMO have made it
into the 5G standard, the signal processing methods required to achieve
unprecedented spectral efficiency have been developed, and the limitation due
to pilot contamination has been resolved. Even the development of fully digital
Massive MIMO arrays for mmWave frequencies - once viewed prohibitively
complicated and costly - is well underway. In a few years, Massive MIMO with
fully digital transceivers will be a mainstream feature at both sub-6 GHz and
mmWave frequencies. In this paper, we explain how the first chapter of the
Massive MIMO research saga has come to an end, while the story has just begun.
The coming wide-scale deployment of BSs with massive antenna arrays opens the
door to a brand new world where spatial processing capabilities are
omnipresent. In addition to mobile broadband services, the antennas can be used
for other communication applications, such as low-power machine-type or
ultra-reliable communications, as well as non-communication applications such
as radar, sensing and positioning. We outline five new Massive MIMO related
research directions: Extremely large aperture arrays, Holographic Massive MIMO,
Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive
MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin
- …