KaSI: a Ka-band and S-band Cross-track Interferometer

A dual-frequency system is needed to better understand natural processes that constitute the environment and seasonal cycles of the Earth. A system working at two different wavelengths acquiring data simultaneously will give a valuable dataset since the conditions on the ground will be exactly the same. Hence, elements such as wind, soil moisture or any other changes on the ground will not interfere in the mea- surements. This thesis explains how an S-band radar was built and tested. Moreover, the experiments done with a Ka-band radar used as a scatterometer are explained as well as the data processing and analysis. Finally, the two systems are used to get dual-frequency measurements from an airborne platform. The dual-frequency data is explored, showing the differences in normalized radar cross-section between frequencies and discussing the interferometric measurements

InSAR Simulations for SWOT and Dual Frequency Processing for Topographic Measurements

In Earth remote sensing precise characterization of the backscatter coefficient is important to extract valuable information about the observed target. A system that eliminates platform motion during near-nadir airborne observations is presented in this thesis, showing an improvement on the accuracy of measurements for a Ka- band scatterometer previously developed at Microwave Remote Sensing Laboratory (MIRSL). These very same results are used to simulate the reflectivity of such targets as seen from a spaceborne radar and estimate height errors based on mission-specific geometry. Finally, data collected from a dual-frequency airborne interferometer com- prised by the Ka-band system and an S-band radar is processed and analyzed to estimate forest heights

Time Domain SAR Processing with GPUs for Airborne Platforms

A time-domain backprojection processor for airborne synthetic aperture radar (SAR) has been developed at the University of Massachusetts’ Microwave Remote Sensing Lab (MIRSL). The aim of this work is to produce a SAR processor capable of addressing the motion compensation issues faced by frequency-domain processing algorithms, in order to create well focused SAR imagery suitable for interferometry. The time-domain backprojection algorithm inherently compensates for non-linear platform motion, dependent on the availability of accurate measurements of the motion. The implementation must manage the relatively high computational burden of the backprojection algorithm, which is done using modern graphics processing units (GPUs), programmed with NVIDIA’s CUDA language. An implementation of the Non-Equispaced Fast Fourier Transform (NERFFT) is used to enable efficient and accurate range interpolation as a critical step of the processing. The phase of time- domain processed imagery is dif erent than that of frequency-domain imagery, leading to a potentially different approach to interferometry. This general purpose SAR processor is designed to work with a novel, dual-frequency S- and Ka-band radar system developed at MIRSL as well as the UAVSAR instrument developed by NASA’s Jet Propulsion Laboratory. These instruments represent a wide range of SAR system parameters, ensuring the ability of the processor to work with most any airborne SAR. Results are presented from these two systems, showing good performance of the processor itself

Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

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Investigation of ground moving target indication techniques for a multi-channel synthetic aperture radar

Synthetic Aperture Radar (SAR) is an imaging technique that creates two dimensional images of the scattering objects in the illuminated ground scene. The objects in the illuminated ground scene may be truly stationary, e.g. buildings etc. or in motion relative to these stationary objects, e.g. cars on a highway. In SAR, the radar platform is moving during the imaging period, hence everything that the radar illuminates has motion relative to the radar platform. In order to specifically detect objects on the ground that are moving relative to stationary ground objects (often termed clutter), processing techniques called Ground Moving Target Indication (GMTI) techniques are required. This is especially required for targets that are moving at relative velocities lower than the stationary clutter's relative velocity to the radar platform (endo-clutter detection). This dissertation investigates five multichannel GMTI techniques being Displaced Phase Centre Antenna (DPCA), Along Track Interferometry (ATI), Iterative Adaptive Approach (IAA), Space Time Adaptive Processing (STAP) and Velocity SAR (VSAR) in literature and assesses the performance of two selected GMTI techniques (ATI and DPCA) on simulated and measured radar data to compare them and identify their strengths and weaknesses. The radar data were measured with a C-band FMCW radar in a controlled environment with known parameters and cooperating targets. The performances of the techniques were assessed in terms of moving target detection within clutter and sensitivity to inaccuracies in the physical system setup. The DPCA technique exhibited some attractive characteristics over the ATI technique. These included its robustness against false alarm in noise dominated cells - ATI exhibited large phase residuals in noise dominated cells, due to the random nature of the phase in these cells. Furthermore, DPCA seem to not suffer from false alarms due to volumetric scattering of vegetation to the extent that was observed with ATI. Lastly, DPCA exhibited more robustness against temporal misalignment errors introduced between the measurement channels, compared to ATI. These observations lead to the conclusion that DPCA would be a practically better choice to implement for the purpose of moving target detection, compared to ATI. However, a double threshold approach, which used DPCA as a pre-processing step to ATI, proved to be superior to DPCA alone in terms of moving target indication within clutter and noise. This approach was verified through implementation on the measured radar data in this study

Motion Estimation and Compensation in Automotive MIMO SAR

With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment. Synthetic Aperture Radar (SAR) systems increase the resolution of conventional mass-market radars by exploiting the vehicle's ego-motion, requiring a very accurate knowledge of the trajectory, usually not compatible with automotive-grade navigation systems. In this regard, this paper deals with the analysis, estimation and compensation of trajectory estimation errors in automotive SAR systems, proposing a complete residual motion estimation and compensation workflow. We start by defining the geometry of the acquisition and the basic processing steps of Multiple-Input Multiple-Output (MIMO) SAR systems. Then, we analytically derive the effects of typical motion errors in automotive SAR imaging. Based on the derived models, the procedure is detailed, outlining the guidelines for its practical implementation. We show the effectiveness of the proposed technique by means of experimental data gathered by a 77 GHz radar mounted in a forward looking configuration.Comment: 14 page

Investigation of Non-coherent Discrete Target Range Estimation Techniques for High-precision Location

Ranging is an essential and crucial task for radar systems. How to solve the range-detection problem effectively and precisely is massively important. Meanwhile, unambiguity and high resolution are the points of interest as well. Coherent and non-coherent techniques can be applied to achieve range estimation, and both of them have advantages and disadvantages. Coherent estimates offer higher precision but are more vulnerable to noise and clutter and phase wrap errors, particularly in a complex or harsh environment, while the non-coherent approaches are simpler but provide lower precision. With the purpose of mitigating inaccuracy and perturbation in range estimation, miscellaneous techniques are employed to achieve optimally precise detection. Numerous elegant processing solutions stemming from non-coherent estimate are now introduced into the coherent realm, and vice versa. This thesis describes two non-coherent ranging estimate techniques with novel algorithms to mitigate the instinct deficit of non-coherent ranging approaches. One technique is based on peak detection and realised by Kth-order Polynomial Interpolation, while another is based on Z-transform and realised by Most-likelihood Chirp Z-transform. A two-stage approach for the fine ranging estimate is applied to the Discrete Fourier transform domain of both algorithms. An N-point Discrete Fourier transform is implemented to attain a coarse estimation; an accurate process around the point of interest determined in the first stage is conducted. For KPI technique, it interpolates around the peak of Discrete Fourier transform profiles of the chirp signal to achieve accurate interpolation and optimum precision. For Most-likelihood Chirp Z-transform technique, the Chirp Z-transform accurately implements the periodogram where only a narrow band spectrum is processed. Furthermore, the concept of most-likelihood estimator is introduced to combine with Chirp Z-transform to acquire better ranging performance. Cramer-Rao lower bound is presented to evaluate the performance of these two techniques from the perspective of statistical signal processing. Mathematical derivation, simulation modelling, theoretical analysis and experimental validation are conducted to assess technique performance. Further research will be pushed forward to algorithm optimisation and system development of a location system using non-coherent techniques and make a comparison to a coherent approach

M-sequenze based ultra-wideband radar and its application to crack detection in salt mines

Die vorliegende Dissertation beschreibt einen innovativen ultra-breitband (UWB)elektromagnetischen Sensor basierend auf einem Pseudo-Rauschverfahren.Der Sensor wurde für zerstörungsfreies Testen in zivilen Anwendungen entwickelt.Zerstörungsfreies Testen entwickelt sich zu einem immer wichtiger werdenden Bereich in Forschung und Entwicklung. Neben unzähligen weiteren Anwendungen und Technologien, besteht ein primäres Aufgabenfeld in der Überwachung und Untersuchung von Bauwerken und Baumaterialien durch berührungslose Messung aus der Ferne.Diese Arbeit konzentriert sich auf das Beispiel der Auflockerungszone im Salzgestein.Der Hintergrund und die Notwendigkeit, den Zustand der oberflächennahen Salzschichten in Salzminen kennen zu müssen, werden beleuchtet und die Messaufgabe anhand einfacher theoretischer Überlegungen beschrieben. Daraus werden die Anforderungen für geeignete UWB Sensoren abgeleitet. Die wichtigsten Eigenschaften sind eine sehr hohe Messband breite sowie eine sehr saubere Systemimpulsantwort frei von systematischen Gerätefehlern. Beide Eigenschaften sind notwendig, um die schwachen Rückstreuungen der Auflockerungen trotz der unvermeidlichen starken Oberflächenreflexion detektieren zu können.Da systematische Fehler bei UWB Geräten technisch nicht von vorne herein komplett vermeidbar sind, muss der Sensor eine Gerätekalibrierung erlauben, um solche Fehler möglichst gut zu unterdrücken.Aufgrund der genannten Anforderungen und den Nebenbedingungen der Messumgebung unter Tage, wurde aus den verschiedenen UWB-Technologien ein Prinzip ausgewählt, welches pseudozufällige Maximalfolgen als Anregungssignal benutzt. Das M-Sequenzkonzept dient als Ausgangpunkt für zahlreiche Weiterentwicklungen. Ein neues Sendemodul erweitert dabei die Messbandbreite auf 12~GHz. Die äquivalente Abtastrate wird um den Faktor vier auf 36~GHz erhöht, ohne den geringen Abtastjitter des ursprünglichen Konzepts zu vergrössern.Weiterhin wird die Umsetzung eines Zweitormesskopfes zur Erfassung von S-Parametern sowie einer automatische Kalibriereinheit beschrieben. Etablierte Kalibrierverfahren aus dem Bereich der Netzwerkanalyse werden kurz rekapituliert und die Adaption des 8-Term Verfahrens mit unbekanntem Transmissionsnormal für das M-Sequenzsystem beschrieben. Dabei werden Kennwerte vorgeschlagen, die dem Bediener unter Tage einfach erlauben, die Kalibrierqualität einzuschätzen und Hinweise auf mögliche Gerätefehler oder andere Probleme zu bekommen. Die Kalibriergenauigkeit des neuen Sensors im Labor wird mit der eines Netzwerkanalysators verglichen. Beide Geräte erreichen eine störungsfreie Dynamik von mehr als 60~dB in den Systemimpulsantworten für Reflexion und Transmission.Der neu entwickelte UWB Sensor wurde in zahlreichen Messungen in Salzminen in Deutschland getestet. Zwei Messbeispiele werden vorgestellt - ein sehr alter, kreisrunder Tunnel sowie ein ovaler Tunnelstumpf, welcher kurz vor den Messungen erst aufgefahren wurde. Messaufbauten und Datenverarbeitung werden beschrieben. Schließlich werden Schlussfolgerungen und Vorschläge für zukünftige Arbeiten mit dem neuen M-Sequenzsensor sowie der Messung von Auflockerungen im Salzgestein diskutiert.This dissertation describes an innovative ultra-wideband (UWB) electromagnetic sensor device based on a pseudo-noise principle developed in the context of non-destructive testing in civil engineering.Non-destructive testing is becoming a more and more important fieldfor researchers and engineers alike. Besides the vast field of possibleapplications and testing technologies, a prime and therefore typical topic is the inspection and monitoringof constructions and materials by means of contactless remote sensing techniques.This work focuses on one example the assessment of the disaggregation zone in salt rock tunnels.The background and relevance of knowing the state of salt rock layers near a tunnel's surface are explainedand simple theoretical considerations for requirements of suitable UWB sensor devices are shown. The most important sensor parameters are a very large measurement bandwidth and a very clean impulse response. The latterparameter translates into the mandatory application of calibration techniques to remove systematic errors of the sensor system itself. This enables detection of weak scattering responses from near-surface disaggregation despite the presence of a strong surface reflection.According to the mentioned requirements and other side conditions in salt mine environments an UWB sensor principlebased on pseudo-noise stimuli namely M-Sequences is selected as a starting point for system development. A newtransmitter frontend for extending the stimulus bandwidth up to 12~GHz is presented. Furthermore, a technique for increasing the (equivalent) sampling rate while keeping the stable and low-jitter sampling regime of the M-Sequencesapproach is introduced and its implementation is shown. Moreover, an automatic calibration unit for full two-port coaxial calibration of the new UWB sensor has been developed. Common calibration techniques from the area of vector network analysers are shortly reviewed and a reasonablealgorithm the 8-term method with an unknown line standard - is selected for the M-Sequences device. The 8-term method is defined in the frequency domain and is adapted for use with time domain devices. Some performance figures and comparisonwith calibration results from network analysers are discussed to show the effectiveness of the calibration.A spurious-free dynamic range of the time domain impulse responses in excess of 60~dB has been achieved for reflection as well as transmission measurements.The new UWB sensor was used in various real world measurements in different salt mines throughout Germany. Two measurementexamples are described and results from the disaggregation zone of a very old and a freshly cut tunnel will be presented. Measurement setup and data processing are discussed and finally some conclusions for future work on this topic are drawn

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