192 research outputs found
Conceptual Study and Performance Analysis of Tandem Dual-Antenna Spaceborne SAR Interferometry
Multi-baseline synthetic aperture radar interferometry (MB-InSAR), capable of
mapping 3D surface model with high precision, is able to overcome the ill-posed
problem in the single-baseline InSAR by use of the baseline diversity. Single
pass MB acquisition with the advantages of high coherence and simple phase
components has a more practical capability in 3D reconstruction than
conventional repeat-pass MB acquisition. Using an asymptotic 3D phase
unwrapping (PU), it is possible to get a reliable 3D reconstruction using very
sparse acquisitions but the interferograms should follow the optimal baseline
design. However, current spaceborne SAR system doesn't satisfy this principle,
inducing more difficulties in practical application. In this article, a new
concept of Tandem Dual-Antenna SAR Interferometry (TDA-InSAR) system for
single-pass reliable 3D surface mapping using the asymptotic 3D PU is proposed.
Its optimal MB acquisition is analyzed to achieve both good relative height
precision and flexible baseline design. Two indicators, i.e., expected relative
height precision and successful phase unwrapping rate, are selected to optimize
the system parameters and evaluate the performance of various baseline
configurations. Additionally, simulation-based demonstrations are conducted to
evaluate the performance in typical scenarios and investigate the impact of
various error sources. The results indicate that the proposed TDA-InSAR is able
to get the specified MB acquisition for the asymptotic 3D PU, which offers a
feasible solution for single-pass 3D SAR imaging.Comment: 16 pages, 20 figure
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
Relationship between Lidar-Derived Canopy Densities and the Scattering Phase Center of High-Resolution TanDEM-X Data
Abstract: The estimation of forestry parameters is essential to understanding the three-dimensional
structure of forests. In this respect, the potential of X-band synthetic aperture radar (SAR) has been
recognized for years. Many studies have been conducted on deriving tree heights with SAR data, but
few have paid attention to the effects of the canopy structure. Canopy density plays an important
role since it provides information about the vertical distribution of dominant scatterers in the forest.
In this study, the position of the scattering phase center (SPC) of interferometric X-band SAR data
is investigated with regard to the densest vegetation layer in a deciduous and coniferous forest in
Germany by applying a canopy density index from high-resolution airborne laser scanning data.
Two different methods defining the densest layer are introduced and compared with the position
of the TanDEM-X SPC. The results indicate that the position of the SPC often coincides with the
densest layer, with mean differences ranging from −1.6 m to +0.7 m in the deciduous forest and
+1.9 m in the coniferous forest. Regarding relative tree heights, the SAR signal on average penetrates
up to 15% (3.4 m) of the average tree height in the coniferous forest. In the deciduous forest, the
difference increases to 18% (6.2 m) during summer and 24% (8.2 m) during winter. These findings
highlight the importance of considering not only tree height but also canopy density when delineating
SAR-based forest heights. The vertical structure of the canopy influences the position of the SPC, and
incorporating canopy density can improve the accuracy of SAR-derived forest height estimations
Applications of bistatic Ku-band radar in snow-covered environments
Bistatic radar imaging is a remote sensing method which employs a spatially separated radio wave transmitter and receiver, in order to construct a two-dimensional image of the reflective properties of objects within a certain area. Compared to the more common monostatic radar systems (which use a co-located transmitter and receiver), bistatic radar systems are considered to be specialized tools which are more suitable for certain specific purposes, at cost of higher complexity. The special-purpose character and higher complexity of bistatic systems cause a low availability of such systems, and thus also of bistatic radar datasets. This is an obstacle for performing studies which require the use of bistatic systems. In the Earth Observation domain, such studies may be aiming, e.g., to explore non-reciprocal scattering processes which do not occur in the monostatic regime, or to investigate phenomena with specific bistatic signatures.
This dissertation makes use of KAPRI, a ground-based Ku-band polarimetric radar interferometer based on the Gamma Portable Radar Interferometer (GPRI). KAPRI was specifically modified by the manufacturer to allow full-polarimetric bistatic radar acquisitions, and can thus be used for studies of the bistatic scattering processes occurring at Ku-band in a variety of environments. The Ku-band frequency of KAPRI makes the study of glacial and snow-covered environments particularly attractive, due to the relatively short but non-zero penetration depth into snow and ice, and due to high interferometric sensitivity to small displacements. In the first part of this thesis, the bistatic operation mode of KAPRI is developed and validated. In the latter two parts, bistatic KAPRI is used to investigate the bistatic scattering properties of snow and ice-covered environments, using two different approaches.
The first part of this thesis focuses on development of the bistatic operation mode of KAPRI, and the associated data processing and polarimetric calibration procedures. A bistatic signal model was developed, which accounts for the offset between the internal oscillators of the two devices forming the bistatic configuration. This offset was compensated through the use of a synchronization link which transmits part of the pulse directly between the two devices. Processing procedures which allow coregistration of bistatic and monostatic datasets were developed through analysis of the elliptical acquisition geometry. The challenge of bistatic polarimetric calibration was resolved through development of a custom active calibration device usable in arbitrary geometries. The associated novel calibration method was compared with the established monostatic procedure, thus validating the novel method for bistatic use.
The second investigation employs KAPRI to study the bistatic scattering properties of snow cover on top of the Great Aletsch Glacier in Switzerland. Two multi-modal time series datasets encompassing full-polarimetric, interferometric, monostatic and bistatic acquisitions were acquired, one in August 2021, and one in March 2022. Analysis of the data revealed considerable differences in polarimetric scattering between the two seasons, caused by the yearly cycle of changing structure of the snow cover. Particular attention was given to polarimetric phase differences, which exhibit a diametrally different response between the two seasons. The results indicate that the co-polar phase difference (CPD) exhibits a smooth, predictable spatial behaviour in summer when the snow cover is firn-like. In winter it exhibits rapid variation and phase-wrapping, thus complicating the use of CPD inversion methods to retrieve snow property information. Analysis of bistatic polarimetric data also revealed the presence of non-reciprocal scattering processes, which manifested itself in the non-zero value of the phase difference between the two cross-polarized polarimetric channels, HV and VH. The temporal coherence of the scene was analyzed and revealed the decorrelation timescale of the snow cover to be between 4-12 hours. This constrains the maximal allowable revisit time for repeat-pass radar methods at Ku-band.
The third investigation of this theses focuses on a specific phenomenon, the coherent backscatter opposition effect (CBOE). We performed the first full bistatic characterization of this effect in the Earth’s cryosphere with a terrestrial sensor (KAPRI) at Ku-band, and with a spaceborne sensor (TanDEM-X) at X-band. The results revealed that the CBOE occurs in terrestrial snow at radio wavelengths, and is detectable at Ku-band in relatively thin seasonal snow layers with thickness of several meters. At X-band the effect was detected in deep firn areas of the Great Aletsch Glacier, indicating the need for a thicker snow layer in order to detect the effect at X-band. Through application of a CBOE scattering model, we were able to relate the angular width and height of the observed enhancement peaks to the scattering and absorption mean free paths of the radio waves within the snow layer. This showcased a possible pathway towards snow parameter estimation through bistatic radar observations of the CBOE
On the Exploitation of CubeSats for Highly Accurate and Robust Single-Pass SAR Interferometry
Highly accurate digital elevation models (DEMs) from spaceborne synthetic aperture radar (SAR) interferometry are often affected by phase unwrapping errors. These errors can be resolved by the use of additional interferograms with different baselines, but this requires additional satellites in a single-pass configuration, resulting in higher cost and system complexity, or additional passes of the satellites, which affects mission planning and makes the system less suitable for monitoring fast-changing phenomena. This work proposes augmenting a bistatic SAR interferometer with one or more receive-only CubeSats, whose images are used to form an additional interferogram with a small baseline, making the system robust to unwrapping errors. In spite of the lower quality of the CubeSat images due to their small antenna aperture, this additional information can be used to detect and resolve phase unwrapping errors in the DEM without impacting its resolution or accuracy. A processing scheme for the phase unwrapping correction is presented along with a theoretical model for its performance. Finally, a design example is presented and discussed along with a simulation based on TanDEM-X data. It is also shown that CubeSat add-ons allow further increasing the baseline and thus improving the accuracy of DEMs. This concept represents a cost-effective solution for the generation of highly accurate, robust DEMs and paves the way to distributed SAR interferometric concepts based on CubeSats
TerraSAR-X Ultra Stable Oscillator Temperature Drift Compensation
After 15 years of successful radar operations in space, the German SAR satellite TerraSAR-X (TSX) showed peculiarities in the frequency of the Ultra Stable Oscillator (USO) since 1st of Nov. 2022 13:56 UTC. In conclusion, we will show at the workshop how we can maintain the excellent performance of TSX despite the new challenges the ageing instrument poses
GPS disciplined RFSoC synchronization, timing, and performance characterization in bistatic radar systems
Distributed radar geometries offer multiple advantages over monostatic pulse-Doppler radar, but synchronizing frequency and timing for transmitting and receiving nodes in a distributed system is required to more accurately detect range and Doppler frequency. A GPS-disciplined bistatic radar synchronization system design running on an RF System-on-Chip (RFSoC) transceiver and GPS-disciplined precision timing reference is detailed, and the features and limitations of these two individual systems are examined. To better understand error tolerances of relevant signals produced by the timing reference used in the synchronization system, in-depth analyses of frequency drift, timing drift, and jitter are conducted and described both with and without GPS disciplining. Custom-designed FPGA IP designed to implement transmit and receive pulse Doppler radar functionality in the RFSoC-based system is introduced
Remote Sensing of Savannas and Woodlands
Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome
Analysis and Design of Joint Communication and Sensing for Wireless Cellular Networks
Joint communication and sensing (JCAS) has emerged as an important piece of technology that will radically change ordinary wireless communication and radar systems. This research area, which has significantly grown over the last decade, aims to develop integrated systems that can provide both communication and sensing/radar functionalities simultaneously. The convergence of both systems into the same joint platform facilitates a more efficient use of the hardware and spectrum resources, enabling new civilian and professional applications.
This thesis focuses on the integration of JCAS functionalities into mobile cellular networks, such as fifth-generation new radio (5G NR) and sixth generation (6G) communication systems, which are developing toward higher frequency ranges at millimeter-wave (mm-wave) bands, coming with wider bandwidths, and have massive antenna arrays, providing a great framework to develop sensing functionalities. By implementing JCAS, the different nodes of the cellular network, such as the base station and user equipment, can sense and reconstruct their surroundings. However, the JCAS operation yields multiple design challenges that need to be addressed. To this end, this thesis aims to develop novel algorithms in two relevant research areas that comprise self-interference (SI) cancellation and beamforming optimization techniques for JCAS systems.
This work analyzes the potential sensing performance of mobile cellular networks, proposing a joint framework and identifying the main radar processing techniques to support JCAS. The fundamental SI challenge stemming from the simultaneous operation of the transmitter and receiver is investigated, and different JCAS cancellation techniques are proposed. The performance and feasibility of the proposed JCAS system is evaluated through simulation and measurement experiments at different frequency bands and scenarios, identifying mm-wave frequencies as the key enabler for future JCAS systems.
Alternative antenna architectures and beamforming methods for mm-wave JCAS platforms are proposed by considering both communication and sensing requirements. Specifically, this thesis proposes novel beamforming methods that provide multiple beams, supporting efficient beamformed communications while an additional beam senses the environment simultaneously. In addition, the proposed beam-forming algorithms address the SI challenge by implementing an efficient spatial suppression scheme to suppress the direct transmitter–receiver coupling
Architectures and Synchronization Techniques for Distributed Satellite Systems: A Survey
Cohesive Distributed Satellite Systems (CDSSs) is a key enabling technology for the future of
remote sensing and communication missions. However, they have to meet strict synchronization requirements before their use is generalized. When clock or local oscillator signals are generated locally at each of the distributed nodes, achieving exact synchronization in absolute phase, frequency, and time is a complex problem. In addition, satellite systems have significant resource constraints, especially for small satellites, which are envisioned to be part of the future CDSSs. Thus, the development of precise, robust, and resource-efficient synchronization techniques is essential for the advancement of future CDSSs. In this context, this survey aims to summarize and categorize the most relevant results on synchronization techniques
for Distributed Satellite Systems (DSSs). First, some important architecture and system concepts are defined. Then, the synchronization methods reported in the literature are reviewed and categorized. This article also provides an extensive list of applications and examples of synchronization techniques for DSSs in addition to the most significant advances in other operations closely related to synchronization, such as inter-satellite ranging and relative position. The survey also provides a discussion on emerging data-driven synchronization techniques based on Machine Learning (ML). Finally, a compilation of current research
activities and potential research topics is proposed, identifying problems and open challenges that can be useful for researchers in the field
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