10 research outputs found
Very High Resolution Tomographic SAR Inversion for Urban Infrastructure Monitoring — A Sparse and Nonlinear Tour
The topic of this thesis is very high resolution (VHR) tomographic SAR inversion for urban infrastructure monitoring. To this end, SAR tomography and differential SAR tomography are demonstrated using TerraSAR-X spotlight data for providing 3-D and 4-D (spatial-temporal) maps of an entire high rise city area including layover separation and estimation of deformation of the buildings. A compressive sensing based estimator (SL1MMER) tailored to VHR SAR data is developed for tomographic SAR inversion by exploiting the sparsity of the signal. A systematic performance assessment of the algorithm is performed regarding elevation estimation accuracy, super-resolution and robustness. A generalized time warp method is proposed which enables differential SAR tomography to estimate multi-component nonlinear motion. All developed methods are validated with both simulated and extensive processing of large volumes of real data from TerraSAR-X
COMPRESSIVE IMAGING AND DUAL MOIRE´ LASER INTERFEROMETER AS METROLOGY TOOLS
Metrology is the science of measurement and deals with measuring different physical aspects of objects. In this research the focus has been on two basic problems that metrologists encounter. The first problem is the trade-off between the range of measurement and the corresponding resolution; measurement of physical parameters of a large object or scene accompanies by losing detailed information about small regions of the object. Indeed, instruments and techniques that perform coarse measurements are different from those that make fine measurements. This problem persists in the field of surface metrology, which deals with accurate measurement and detailed analysis of surfaces. For example, laser interferometry is used for fine measurement (in nanometer scale) while to measure the form of in object, which lies in the field of coarse measurement, a different technique like moire technique is used. We introduced a new technique to combine measurement from instruments with better resolution and smaller measurement range with those with coarser resolution and larger measurement range. We first measure the form of the object with coarse measurement techniques and then make some fine measurement for features in regions of interest. The second problem is the measurement conditions that lead to difficulties in measurement. These conditions include low light condition, large range of intensity variation, hyperspectral measurement, etc. Under low light condition there is not enough light for detector to detect light from object, which results in poor measurements. Large range of intensity variation results in a measurement with some saturated regions on the camera as well as some dark regions. We use compressive sampling based imaging systems to address these problems. Single pixel compressive imaging uses a single detector instead of array of detectors and reconstructs a complete image after several measurements. In this research we examined compressive imaging for different applications including low light imaging, high dynamic range imaging and hyperspectral imaging
Recommended from our members
Time-domain Compressive Beamforming for Medical Ultrasound Imaging
Over the past 10 years, Compressive Sensing has gained a lot of visibility from the medical imaging research community. The most compelling feature for the use of Compressive Sensing is its ability to perform perfect reconstructions of under-sampled signals using l1-minimization. Of course, that counter-intuitive feature has a cost. The lacking information is compensated for by a priori knowledge of the signal under certain mathematical conditions. This technology is currently used in some commercial MRI scanners to increase the acquisition rate hence decreasing discomfort for the patient while increasing patient turnover. For echography, the applications could go from fast 3D echocardiography to simplified, cheaper echography systems.
Real-time ultrasound imaging scanners have been available for nearly 50 years. During these 50 years of existence, much has changed in their architecture, electronics, and technologies. However one component remains present: the beamformer. From analog beamformers to software beamformers, the technology has evolved and brought much diversity to the world of beam formation. Currently, most commercial scanners use several focalized ultrasonic pulses to probe tissue. The time between two consecutive focalized pulses is not compressible, limiting the frame rate. Indeed, one must wait for a pulse to propagate back and forth from the probe to the deepest point imaged before firing a new pulse.
In this work, we propose to outline the development of a novel software beamforming technique that uses Compressive Sensing. Time-domain Compressive Beamforming (t-CBF) uses computational models and regularization to reconstruct de-cluttered ultrasound images. One of the main features of t-CBF is its use of only one transmit wave to insonify the tissue. Single-wave imaging brings high frame rates to the modality, for example allowing a physician to see precisely the movements of the heart walls or valves during a heart cycle. t-CBF takes into account the geometry of the probe as well as its physical parameters to improve resolution and attenuate artifacts commonly seen in single-wave imaging such as side lobes.
In this thesis, we define a mathematical framework for the beamforming of ultrasonic data compatible with Compressive Sensing. Then, we investigate its capabilities on simple simulations in terms of resolution and super-resolution. Finally, we adapt t-CBF to real-life ultrasonic data. In particular, we reconstruct 2D cardiac images at a frame rate 100-fold higher than typical values
Arrayed LiDAR signal analysis for automotive applications
Light detection and ranging (LiDAR) is one of the enabling technologies for advanced
driver assistance and autonomy. Advances in solid-state photon detector arrays offer
the potential of high-performance LiDAR systems but require novel signal processing
approaches to fully exploit the dramatic increase in data volume an arrayed detector
can provide.
This thesis presents two approaches applicable to arrayed solid-state LiDAR. First, a
novel block independent sparse depth reconstruction framework is developed, which
utilises a random and very sparse illumination scheme to reduce illumination density while improving sampling times, which further remain constant for any array
size. Compressive sensing (CS) principles are used to reconstruct depth information
from small measurement subsets. The smaller problem size of blocks reduces the
reconstruction complexity, improves compressive depth reconstruction performance
and enables fast concurrent processing. A feasibility study of a system proposal for
this approach demonstrates that the required logic could be practically implemented
within detector size constraints. Second, a novel deep learning architecture called
LiDARNet is presented to localise surface returns from LiDAR waveforms with high
throughput. This single data driven processing approach can unify a wide range
of scenarios, making use of a training-by-simulation methodology. This augments
real datasets with challenging simulated conditions such as multiple returns and
high noise variance, while enabling rapid prototyping of fast data driven processing
approaches for arrayed LiDAR systems.
Both approaches are fast and practical processing methodologies for arrayed LiDAR
systems. These retrieve depth information with excellent depth resolution for wide
operating ranges, and are demonstrated on real and simulated data. LiDARNet is
a rapid approach to determine surface locations from LiDAR waveforms for efficient point cloud generation, while block sparse depth reconstruction is an efficient method to facilitate high-resolution depth maps at high frame rates with reduced power and memory requirements.Engineering and Physical Sciences Research Council (EPSRC
Compressive Sensing and Its Applications in Automotive Radar Systems
Die Entwicklung in Richtung zu autonomem Fahren verspricht, künftig einen sicheren
Verkehr ohne tödliche Unfälle zu ermöglichen, indem menschliche Fahrer vollständig
ersetzt werden. Dadurch entfällt der Faktor des menschlichen Fehlers, der aus
Müdigkeit, Unachtsamkeit oder Alkoholeinfluss resultiert. Um jedoch eine breite
Akzeptanz für autonome Fahrzeuge zu erreichen und es somit eines Tages vollständig
umzusetzen, sind noch eine Vielzahl von Herausforderungen zu lösen. Da in einem
autonomen Fahrzeug kein menschlicher Fahrer mehr in Notfällen eingreifen kann,
müssen sich autonome Fahrzeuge auf leistungsfähige und robuste Sensorsysteme
verlassen können, um in kritischen Situationen auch unter widrigen Bedingungen
angemessen reagieren zu können. Daher ist die Entwicklung von Sensorsystemen
erforderlich, die für Funktionalitäten jenseits der aktuellen advanced driver assistance
systems eingesetzt werden können. Dies resultiert in neuen Anforderungen, die erfüllt
werden müssen, um sichere und zuverlässige autonome Fahrzeuge zu realisieren, die
weder Fahrzeuginsassen noch Passanten gefährden. Radarsysteme gehören zu den
Schlüsselkomponenten unter der Vielzahl der verfügbaren Sensorsysteme, da sie im
Gegensatz zu visuellen Sensoren von widrigen Wetter- und Umgebungsbedingungen
kaum beeinträchtigt werden. Darüber hinaus liefern Radarsysteme zusätzliche
Umgebungsinformationen wie Abstand, Winkel und relative Geschwindigkeit zwischen
Sensor und reflektierenden Zielen. Die vorliegende Dissertation deckt im Wesentlichen
zwei Hauptaspekte der Forschung und Entwicklung auf dem Gebiet der Radarsysteme
im Automobilbereich ab. Ein Aspekt ist die Steigerung der Effizienz und Robustheit
der Signalerfassung und -verarbeitung für die Radarperzeption. Der andere Aspekt ist
die Beschleunigung der Validierung und Verifizierung von automated cyber-physical
systems, die parallel zum Automatisierungsgrad auch eine höhere Komplexität
aufweisen.
Nach der Analyse zahlreicher möglicher Compressive Sensing Methoden, die im
Bereich Fahrzeugradarsysteme angewendet werden können, wird ein rauschmoduliertes
gepulstes Radarsystem vorgestellt, das kommerzielle Fahrzeugradarsysteme in
seiner Robustheit gegenüber Rauschen übertrifft. Die Nachteile anderer gepulster
Radarsysteme hinsichtlich des Signalerfassungsaufwands und der Laufzeit werden
durch die Verwendung eines Compressive Sensing-Signalerfassungs- und Rekonstruktionsverfahrens
in Kombination mit einer Rauschmodulation deutlich verringert.
Mit Compressive Sensing konnte der Aufwand für die Signalerfassung um 70% reduziert
werden, während gleichzeitig die Robustheit der Radarwahrnehmung auch für signal-to-noise-ratio-Pegel nahe oder unter Null erreicht wird. Mit einem validierten
Radarsensormodell wurde das Rauschradarsystem emuliert und mit einem
kommerziellen Fahrzeugradarsystem verglichen. Datengetriebene Wettermodelle
wurden entwickelt und während der Simulation angewendet, um die Radarleistung
unter widrigen Bedingungen zu bewerten. Während eine Besprühung mit Wasser die
Radomdämpfung um 10 dB erhöht und Spritzwasser sogar um 20 dB, ergibt sich die
eigentliche Begrenzung aus der Rauschzahl und Empfindlichkeit des Empfängers. Es
konnte bewiesen werden, dass das vorgeschlagene Compressive Sensing Rauschradarsystem
mit einer zusätzlichen Signaldämpfung von bis zu 60 dB umgehen kann
und damit eine hohe Robustheit in ungünstigen Umwelt- und Wetterbedingungen
aufweist.
Neben der Robustheit wird auch die Interferenz berücksichtigt. Zum einen wird
die erhöhte Störfestigkeit des Störradarsystems nachgewiesen. Auf der anderen
Seite werden die Auswirkungen auf bestehende Fahrzeugradarsysteme bewertet und
Strategien zur Minderung der Auswirkungen vorgestellt.
Die Struktur der Arbeit ist folgende. Nach der Einführung der Grundlagen
und Methoden für Fahrzeugradarsysteme werden die Theorie und Metriken hinter
Compressive Sensing gezeigt. Darüber hinaus werden weitere Aspekte wie Umgebungsbedingungen,
unterschiedliche Radararchitekturen und Interferenz erläutert.
Der Stand der Technik gibt einen Überblick über Compressive Sensing-Ansätze und
Implementierungen mit einem Fokus auf Radar. Darüber hinaus werden Aspekte
von Fahrzeug- und Rauschradarsystemen behandelt. Der Hauptteil beginnt mit
der Vorstellung verschiedener Ansätze zur Nutzung von Compressive Sensing für
Fahrzeugradarsysteme, die in der Lage sind, die Erfassung und Wahrnehmung von
Radarsignalen zu verbessern oder zu erweitern. Anschließend wird der Fokus auf
ein Rauschradarsystem gelegt, das mit Compressive Sensing eine effiziente Signalerfassung
und -rekonstruktion ermöglicht. Es wurde mit verschiedenen Compressive
Sensing-Metriken analysiert und in einer Proof-of-Concept-Simulation bewertet. Mit
einer Emulation des Rauschradarsystems wurde das Potential der Compressive Sensing
Signalerfassung und -verarbeitung in einem realistischeren Szenario demonstriert.
Die Entwicklung und Validierung des zugrunde liegenden Sensormodells wird ebenso
dokumentiert wie die Entwicklung der datengetriebenen Wettermodelle. Nach der
Betrachtung von Interferenz und der Koexistenz des Rauschradars mit kommerziellen
Radarsystemen schließt ein letztes Kapitel mit Schlussfolgerungen und einem
Ausblick die Arbeit ab.Developments towards autonomous driving promise to lead to safer traffic, where fatal
accidents can be avoided after making human drivers obsolete and hence removing
the factor of human error. However, to ensure the acceptance of automated driving
and make it a reality one day, still a huge amount of challenges need to be solved.
With having no human supervisors, automated vehicles have to rely on capable and
robust sensor systems to ensure adequate reactions in critical situations, even during
adverse conditions. Therefore, the development of sensor systems is required that
can be applied for functionalities beyond current advanced driver assistance systems.
New requirements need to be met in order to realize safe and reliable automated
vehicles that do not harm passersby.
Radar systems belong to the key components among the variety of sensor systems.
Other than visual sensors, radar is less vulnerable towards adverse weather and
environment conditions. In addition, radar provides complementary environment
information such as target distance, angular position or relative velocity, too. The
thesis ad hand covers basically two main aspects of research and development in the
field of automotive radar systems. One aspect is to increase efficiency and robustness
in signal acquisition and processing for radar perception. The other aspect is to
accelerate validation and verification of automated cyber-physical systems that
feature more complexity along with the level of automation.
After analyzing a variety of possible Compressive Sensing methods for automotive
radar systems, a noise modulated pulsed radar system is suggested in the thesis at
hand, which outperforms commercial automotive radar systems in its robustness
towards noise. Compared to other pulsed radar systems, their drawbacks regarding
signal acquisition effort and computation run time are resolved by using noise modulation
for implementing a Compressive Sensing signal acquisition and reconstruction
method. Using Compressive Sensing, the effort in signal acquisition was reduced by
70%, while obtaining a radar perception robustness even for signal-to-noise-ratio
levels close to or below zero. With a validated radar sensor model the noise radar
was emulated and compared to a commercial automotive radar system. Data-driven
weather models were developed and applied during simulation to evaluate radar performance
in adverse conditions. While water sprinkles increase radome attenuation
by 10 dB and splash water even by 20 dB, the actual limitation comes from noise
figure and sensitivity of the receiver. The additional signal attenuation that can be
handled by the proposed compressive sensing noise radar system proved to be even up to 60 dB, which ensures a high robustness of the receiver during adverse weather
and environment conditions.
Besides robustness, interference is also considered. On the one hand the increased
robustness towards interference of the noise radar system is demonstrated. On
the other hand, the impact on existing automotive radar systems is evaluated and
strategies to mitigate the impact are presented.
The structure of the thesis is the following. After introducing basic principles
and methods for automotive radar systems, the theory and metrics of Compressive
Sensing is presented. Furthermore some particular aspects are highlighted such as
environmental conditions, different radar architectures and interference. The state of
the art provides an overview on Compressive Sensing approaches and implementations
with focus on radar. In addition, it covers automotive radar and noise radar related
aspects. The main part starts with presenting different approaches on making use
of Compressive Sensing for automotive radar systems, that are capable of either
improving or extending radar signal acquisition and perception. Afterwards the focus
is put on a noise radar system that uses Compressive Sensing for an efficient signal
acquisition and reconstruction. It was analyzed using different Compressive Sensing
metrics and evaluated in a proof-of-concept simulation. With an emulation of the
noise radar system the feasibility of the Compressive Sensing signal acquisition and
processing was demonstrated in a more realistic scenario. The development and
validation of the underlying sensor model is documented as well as the development
of the data-driven weather models. After considering interference and co-existence
with commercial radar systems, a final chapter with conclusions and an outlook
completes the work
AI for time-resolved imaging: from fluorescence lifetime to single-pixel time of flight
Time-resolved imaging is a field of optics which measures the arrival time of light on the camera. This thesis looks at two time-resolved imaging modalities: fluorescence lifetime imaging and time-of-flight measurement for depth imaging and ranging. Both of these applications require temporal accuracy on the order of pico- or nanosecond (10−12 − 10−9s) scales.
This demands special camera technology and optics that can sample light-intensity extremely quickly, much faster than an ordinary video camera. However, such detectors can be very expensive compared to regular cameras while offering lower image quality. Further, information of interest is often hidden (encoded) in the raw temporal data. Therefore, computational imaging algorithms are used to enhance, analyse and extract information from time-resolved images.
"A picture is worth a thousand words". This describes a fundamental blessing and curse of image analysis: images contain extreme amounts of data. Consequently, it is very difficult to design algorithms that encompass all the possible pixel permutations and combinations that can encode this information. Fortunately, the rise of AI and machine learning (ML) allow us to instead create algorithms in a data-driven way. This thesis demonstrates the application of ML to time-resolved imaging tasks, ranging from parameter estimation in noisy data and decoding of overlapping information, through super-resolution, to inferring 3D information from 1D (temporal) data
Compressed Sensing in Multi-Signal Environments.
Technological advances and the ability to build cheap high performance sensors make it possible to deploy tens or even hundreds of sensors to acquire information about a common phenomenon of interest. The increasing number of sensors allows us to acquire ever more detailed information about the underlying scene that was not possible before. This, however, directly translates to increasing amounts of data that needs to be acquired, transmitted, and processed. The amount of data can be overwhelming, especially in applications that involve high-resolution signals such as images or videos. Compressed sensing (CS) is a novel acquisition and reconstruction scheme that is particularly useful in scenarios when high resolution signals are difficult or expensive to encode. When applying CS in a multi-signal scenario, there are several aspects that need to be considered such as the sensing matrix, the joint signal model, and the reconstruction algorithm. The purpose of this dissertation is to provide a complete treatment of these aspects in various multi-signal environments. Specific applications include video, multi-view imaging, and structural health monitoring systems. For each application, we propose a novel joint signal model that accurately captures the joint signal structure, and we tailor the reconstruction algorithm to each signal model to successfully recover the signals of interest.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/98007/1/jaeypark_1.pd
Intelligent Sensor Networks
In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts