121 research outputs found
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
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
BDS GNSS for Earth Observation
For millennia, human communities have wondered about the possibility of observing
phenomena in their surroundings, and in particular those affecting the Earth on which they live.
More generally, it can be conceptually defined as Earth observation (EO) and is the collection of
information about the biological, chemical and physical systems of planet Earth. It can be undertaken
through sensors in direct contact with the ground or airborne platforms (such as weather balloons and
stations) or remote-sensing technologies. However, the definition of EO has only become significant
in the last 50 years, since it has been possible to send artificial satellites out of Earth’s orbit.
Referring strictly to civil applications, satellites of this type were initially designed to provide
satellite images; later, their purpose expanded to include the study of information on land
characteristics, growing vegetation, crops, and environmental pollution. The data collected are used
for several purposes, including the identification of natural resources and the production of accurate
cartography. Satellite observations can cover the land, the atmosphere, and the oceans.
Remote-sensing satellites may be equipped with passive instrumentation such as infrared or
cameras for imaging the visible or active instrumentation such as radar. Generally, such satellites are
non-geostationary satellites, i.e., they move at a certain speed along orbits inclined with respect to the
Earth’s equatorial plane, often in polar orbit, at low or medium altitude, Low Earth Orbit (LEO) and
Medium Earth Orbit (MEO), thus covering the entire Earth’s surface in a certain scan time (properly
called ’temporal resolution’), i.e., in a certain number of orbits around the Earth.
The first remote-sensing satellites were the American NASA/USGS Landsat Program;
subsequently, the European: ENVISAT (ENVironmental SATellite), ERS (European Remote-Sensing
satellite), RapidEye, the French SPOT (Satellite Pour l’Observation de laTerre), and the Canadian
RADARSAT satellites were launched. The IKONOS, QuickBird, and GeoEye-1 satellites were
dedicated to cartography. The WorldView-1 and WorldView-2 satellites and the COSMO-SkyMed
system are more recent. The latest generation are the low payloads called Small Satellites, e.g., the
Chinese BuFeng-1 and Fengyun-3 series.
Also, Global Navigation Satellite Systems (GNSSs) have captured the attention of researchers
worldwide for a multitude of Earth monitoring and exploration applications. On the other hand,
over the past 40 years, GNSSs have become an essential part of many human activities. As is widely
noted, there are currently four fully operational GNSSs; two of these were developed for military
purposes (American NAVstar GPS and Russian GLONASS), whilst two others were developed for
civil purposes such as the Chinese BeiDou satellite navigation system (BDS) and the European
Galileo. In addition, many other regional GNSSs, such as the South Korean Regional Positioning
System (KPS), the Japanese quasi-zenital satellite system (QZSS), and the Indian Regional Navigation
Satellite System (IRNSS/NavIC), will become available in the next few years, which will have
enormous potential for scientific applications and geomatics professionals.
In addition to their traditional role of providing global positioning, navigation, and timing (PNT)
information, GNSS navigation signals are now being used in new and innovative ways. Across the
globe, new fields of scientific study are opening up to examine how signals can provide information
about the characteristics of the atmosphere and even the surfaces from which they are reflected before
being collected by a receiver.
EO researchers monitor global environmental systems using in situ and remote monitoring tools.
Their findings provide tools to support decision makers in various areas of interest, from security
to the natural environment. GNSS signals are considered an important new source of information
because they are a free, real-time, and globally available resource for the EO community
Static Background Removal in Vehicular Radar: Filtering in Azimuth-Elevation-Doppler Domain
A significant challenge in autonomous driving systems lies in image
understanding within complex environments, particularly dense traffic
scenarios. An effective solution to this challenge involves removing the
background or static objects from the scene, so as to enhance the detection of
moving targets as key component of improving overall system performance. In
this paper, we present an efficient algorithm for background removal in
automotive radar applications, specifically utilizing a frequency-modulated
continuous wave (FMCW) radar. Our proposed algorithm follows a three-step
approach, encompassing radar signal preprocessing, three-dimensional (3D)
ego-motion estimation, and notch filter-based background removal in the
azimuth-elevation-Doppler domain. To begin, we model the received signal of the
FMCW multiple-input multiple-output (MIMO) radar and develop a signal
processing framework for extracting four-dimensional (4D) point clouds.
Subsequently, we introduce a robust 3D ego-motion estimation algorithm that
accurately estimates radar ego-motion speed, accounting for Doppler ambiguity,
by processing the point clouds. Additionally, our algorithm leverages the
relationship between Doppler velocity, azimuth angle, elevation angle, and
radar ego-motion speed to identify the spectrum belonging to background
clutter. Subsequently, we employ notch filters to effectively filter out the
background clutter. The performance of our algorithm is evaluated using both
simulated data and extensive experiments with real-world data. The results
demonstrate its effectiveness in efficiently removing background clutter and
enhacing perception within complex environments. By offering a fast and
computationally efficient solution, our approach effectively addresses
challenges posed by non-homogeneous environments and real-time processing
requirements
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
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
Coexistence Designs of Radar and Communication Systems in a Multi-path Scenario
The focus of this study is on the spectrum sharing between multiple-input
multiple-output (MIMO) communications and co-located MIMO radar systems in
multi-path environments. The major challenge is to suppress the mutual
interference between the two systems while combining the useful multi-path
components received at each system. We tackle this challenge by jointly
designing the communication precoder, radar transmit waveform and receive
filter. Specifically, the signal-to-interference-plus-noise ratio (SINR) at the
radar receiver is maximized subject to constraints on the radar waveform,
communication rate and transmit power. The multi-path propagation complicates
the expressions of the radar SINR and communication rate, leading to a
non-convex problem. To solve it, a sub-optimal algorithm based on the
alternating maximization is used to optimize the precoder, radar transmit
waveform and receive filter iteratively. Simulation results are provided to
demonstrate the effectiveness of the proposed design
Millimetre-wave radar development for high resolution detection
Automotive technology today is focusing on autonomous vehicle development. The
sensors for these systems include radars due to their robustness against adverse
weather conditions such as rain, fog, ash or snow. In this constant search for advancement, high resolution systems play a central role in target detection and avoidance. In this PhD project, these methods have been researched and engineered to
leverage the best radar resolution for collision avoidance systems.
The first part of this thesis will focus on the existing systems consisting of the
state-of-the-art at the time of writing and explain what makes a high resolution
radar and how it can cover the whole field of view. The second part will focus on
how a non-uniform sparse radar system was simulated, developed and benchmarked
for improved radar performance up to 40% better than conventional designs. The
third part will focus on signal processing techniques and how these methods have
achieved high resolution and detection: large virtual aperture array using Multiple
Input Multiple Output (MIMO) systems, beampattern multiplication to improve
side-lobe levels and compressive sensing. Also, the substrate-integrated waveguide
(SIW) antennas which have been fabricated provide a bandwidth of 1.5GHz for the
transmitter and 2GHz at the receiver. This has resulted in a range resolution of 10
cm. The four part of this thesis presents the measurements which have been carried
out at the facilities within Heriot-Watt University and also at Netherlands Organisation for Applied Scientific Research (TNO). The results were better than expected
since a two transmitter four receiver system was able to detect targets which have
been separated at 2.2◦
in angle in the horizontal plane. Also, compressive sensing was used as a high resolution method for obtaining fine target detection and
in combination with the multiplication method showed improved detection performance with a 20 dB side-lobe level suppression. The measurement results from the
6-months placements are presented and compared with the state-of the art, revealing that the developed radar is comparable in performance to high-grade automotive
radars developed in the industry
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