Single-look light-burden superresolution differential SAR tomography

Research and application is spreading of techniques of coherent combination of complex-valued synthetic aperture radar (SAR) data to extract rich information even on complex observed scenes, fully exploiting existing SAR data archives, and new satellites. Among such techniques, SAR tomography stems from multibaseline interferometry to achieve full-3D imaging through elevation beamforming (spatial spectral estimation). The Tomo concept has been integrated with the mature differential interferometry, producing the new differential tomography (Diff-Tomo) processing mode, that allows `opening' the SAR cells in complex non-stationary scenes, resolving multiple heights and slow deformation velocities of layover scatterers. Consequently, the operational capability limit of differential interferometry to the single scatterer case is overcome. Diff-Tomo processing is cast in a 2D baseline-time spectral analysis framework, with sparse sampling. The use of adaptive 2D spectral estimation has demonstrated to allow joint baseline-time processing with reduced sidelobes and enhanced height-velocity resolution at low computational burden. However, this method requires coherent multilooking processing, thus does not produce full range-azimuth resolution products, as it would be desirable for urban applications. A new single-look adaptive Diff-Tomo processor is presented and tested with satellite data, allowing full range-azimuth resolution together with height-velocity sidelobe reduction and superresolution capabilities and the low computational burden

Arrayed synthetic aperture radar

In this thesis, the use of array processing techniques applied to Single Input Multiple Output (SIMO) SAR systems with enhanced capabilities is investigated. In Single Input Single Output (SISO) SAR systems there is a high resolution, wide swath contradiction, whereby it is not possible to increase both cross-range resolution and the imaged swath width simultaneously. To overcome this, a novel beamformer for SAR systems in the cross-range direction is proposed. In particular, this beamformer is a superresolution beamformer capable of forming wide nulls using subspace based approaches. SIMO SAR systems also give rise to additional sets of received data, which includes geometrical information about the SAR and target environment, and can be used for enhanced target parameter estimation. In particular, this thesis looks at round trip delay, joint azimuth and elevation angle, and relative target power estimation. For round trip delay estimation, the use of the traditional matched filter with subspace partitioning is proposed. Then by using a joint 2D Multiple Signal Classification (MUSIC) algorithm, joint Direction of Arrival (DOA) estimation can be achieved. Both the use of range lines of raw SAR data and the use of a Region of Interest (ROI) of a SAR image are investigated. However in terms of imaging, MUSIC is not well-suited for SAR, due to its target response not corresponding to the target's true power return. Therefore a joint DOA and target power estimation algorithm is proposed to overcome this limitation. These algorithms provide the framework for the development of three processing techniques. These allow sidelobe suppression in the slant range direction, along with the reconstruction of undersampled data and region enhancement using MUSIC with power preservation.Open Acces

Bayesian super-resolution with application to radar target recognition

This thesis is concerned with methods to facilitate automatic target recognition using images generated from a group of associated radar systems. Target recognition algorithms require access to a database of previously recorded or synthesized radar images for the targets of interest, or a database of features based on those images. However, the resolution of a new image acquired under non-ideal conditions may not be as good as that of the images used to generate the database. Therefore it is proposed to use super-resolution techniques to match the resolution of new images with the resolution of database images. A comprehensive review of the literature is given for super-resolution when used either on its own, or in conjunction with target recognition. A new superresolution algorithm is developed that is based on numerical Markov chain Monte Carlo Bayesian statistics. This algorithm allows uncertainty in the superresolved image to be taken into account in the target recognition process. It is shown that the Bayesian approach improves the probability of correct target classification over standard super-resolution techniques. The new super-resolution algorithm is demonstrated using a simple synthetically generated data set and is compared to other similar algorithms. A variety of effects that degrade super-resolution performance, such as defocus, are analyzed and techniques to compensate for these are presented. Performance of the super-resolution algorithm is then tested as part of a Bayesian target recognition framework using measured radar data

Scattering Center Extraction and Recognition Based on ESPRIT Algorithm

Inverse Synthetic Aperture Radar (ISAR) generates high quality radar images even in low visibility. And it provides important physical features for space target recognition and location. This thesis focuses on ISAR rapid imaging, scattering center information extraction, and target classification. Based on the principle of Fourier imaging, the backscattering field of radar target is obtained by physical optics (PO) algorithm, and the relation between scattering field and objective function is deduced. According to the resolution formula, the incident parameters of electromagnetic wave are set reasonably. The interpolation method is used to realize three-dimensional (3D) simulation of aircraft target, and the results are compared with direct imaging results. CLEAN algorithm extracts scattering center information effectively. But due to the limitation of resolution parameters, traditional imaging can’t meet the actual demand. Therefore, the super-resolution Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm is used to obtain spatial target location information. The signal subspace and noise subspace are orthogonal to each other. By combining spatial smoothing method with ESPRIT algorithm, the physical characteristics of geometric target scattering center are obtained accurately. In particular, the proposed method is validated on complex 3D aircraft targets and it proves that this method is applied to most scattering mechanisms. The distribution of scattering centers reflects the geometric information of the target. Therefore, the electromagnetic image to be recognized and ESPRIT image are matched by the domain matching method. And the classification results under different radii are obtained. In addition, because the neural network can extract rich image features, the improved ALEX network is used to classify and recognize target data processed by ESPRIT. It proves that ESPRIT algorithm can be used to expand the existing datasets and prepare for future identification of targets in real environments. Final a visual classification system is constructed to visually display the results

Mismatched Processing for Radar Interference Cancellation

Matched processing is a fundamental filtering operation within radar signal processing to estimate scattering in the radar scene based on the transmit signal. Although matched processing maximizes the signal-to-noise ratio (SNR), the filtering operation is ineffective when interference is captured in the receive measurement. Adaptive interference mitigation combined with matched processing has proven to mitigate interference and estimate the radar scene. A known caveat of matched processing is the resulting sidelobes that may mask other scatterers. The sidelobes can be efficiently addressed by windowing but this approach also comes with limited suppression capabilities, loss in resolution, and loss in SNR. The recent emergence of mismatch processing has shown to optimally reduce sidelobes while maintaining nominal resolution and signal estimation performance. Throughout this work, re-iterative minimum-mean square error (RMMSE) adaptive and least-squares (LS) optimal mismatch processing are proposed for enhanced signal estimation in unison with adaptive interference mitigation for various radar applications including random pulse repetition interval (PRI) staggering pulse-Doppler radar, airborne ground moving target indication, and radar & communication spectrum sharing. Mismatch processing and adaptive interference cancellation each can be computationally complex for practical implementation. Sub-optimal RMMSE and LS approaches are also introduced to address computational limitations. The efficacy of these algorithms is presented using various high-fidelity Monte Carlo simulations and open-air experimental datasets

Direction of Arrival Estimation in Low-Cost Frequency Scanning Array Antenna Systems

RÉSUMÉ Cette thèse propose des méthodes d'estimation de la direction d'arrivée (DOA) et d'amélioration de la résolution angulaire applicables aux antennes à balayage de fréquence (Frequency Scanning Antenna ou FSA) et présente un développement analytique et des confirmations expérimentales des méthodes proposées. Les FSA sont un sous-ensemble d'antennes à balayage électronique dont l'angle du faisceau principal change en faisant varier la fréquence des signaux. L'utilisation des FSA est un compromis entre des antennes à balayage de phase (phased arrays antennas) plus coûteuses et plus complexes, et des antennes à balayage mécanique plus lentes et non agiles. Bien que l'agilité et le faible coût des FSA les rendent un choix plausible dans certaines applications, les FSA à faible coût peuvent ne pas être conformes aux exigences souhaitées pour l'application cible telles que les exigences de résolution angulaire. Ainsi, cette recherche tente d'abord de caractériser les capacités de résolution angulaire de certains systèmes d'antennes FSA sélectionnés. Elle poursuit en explorant des modifications ou extensions aux algorithmes de super-résolution capables d'améliorer la résolution angulaire de l'antenne et de les adapter pour être appliqués aux FSA. Deux méthodes d'estimation de la résolution angulaire, l'estimation du maximum de vraisemblance (Maximum Likelihood ou ML) et la formation du faisceau de variance minimale de Capon (Minimum Variance Beamforming ou MVB) sont étudiées dans cette recherche. Les deux méthodes sont modifiées pour être applicables aux FSA. De plus, les méthodes d'étalonnage et de pré-traitement requises pour chaque méthode sont également introduites. Les résultats de simulation ont montré qu'en sélectionnant des paramètres corrects, il est possible d'améliorer la résolution angulaire au-delà de la limitation de la largeur de faisceau des FSA en utilisant les deux méthodes. Les critères pour lesquels chaque méthode fonctionne le mieux sont discutés et l'analyse pour justifier les conditions présentées est donnée.----------ABSTRACT This research investigates direction of arrival (DOA) estimation and angular resolution enhancement methods applicable to frequency scanning antennas (FSA) and provides analytical development and experimental validation for the proposed methods. FSAs are a subset of electronically scanning antennas, which scan the angle of their main beam by varying the frequency of the signals. Using FSA is a trade-off between more expensive and complex phase array antennas and slower and non-agile mechanical scanning antennas. Although agility and low-cost of FSAs make them a plausible choice in some application, low-cost FSAs may not comply with the desired requirements for the target application such as angular resolution requirements. Thus, this research attempts to first characterize the angular resolution capabilities of some selected FSA antenna systems, and then modify or extend super-resolution algorithms capable of enhancing the angular resolution of the antenna and adapt them to be applied to FSAs. Two angular resolution estimation methods, maximum likelihood estimation (ML) and Capon minimum variance beamforming (MVB), are studied in this research. Both methods are modified to be applicable to FSAs. In addition, the calibration and pre-processing methods required for each method are also introduced. Simulation results show that by selecting correct parameters, it is possible to enhance angular resolution beyond the beamwidth limitation of FSAs using both methods. The criteria for which each method performs the best are discussed and an analysis supporting the presented conditions are given. The proposed methods are also validated using the measured antenna radiation pattern of an 8-element FSA which is built based on a composite right/left-handed (CRLH) waveguide. In addition, the experimental results using a beam scanning parabolic reflector antenna using a frequency multiplexed antenna feed is given. The design limitations of this antenna reduces the performance of angular resolution enhancement methods. Therefore, a hybrid scanning system combining mechanical and frequency scanning using the beam scanning reflector antenna is also proposed

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

Fourier optics approaches to enhanced depth-of-field applications in millimetre-wave imaging and microscopy

In the first part of this thesis millimetre-wave interferometric imagers are considered for short-range applications such as concealed weapons detection. Compared to real aperture systems, synthetic aperture imagers at these wavelengths can provide improvements in terms of size, cost, depth-of-field (DoF) and imaging flexibility via digitalrefocusing. Mechanical scanning between the scene and the array is investigated to reduce the number of antennas and correlators which drive the cost of such imagers. The tradeoffs associated with this hardware reduction are assessed before to jointly optimise the array configuration and scanning motion. To that end, a novel metric is proposed to quantify the uniformity of the Fourier domain coverage of the array and is maximised with a genetic algorithm. The resulting array demonstrates clear improvements in imaging performances compared to a conventional power-law Y-shaped array. The DoF of antenna arrays, analysed via the Strehl ratio, is shown to be limited even for infinitely small antennas, with the exception of circular arrays. In the second part of this thesis increased DoF in optical systems with Wavefront Coding (WC) is studied. Images obtained with WC are shown to exhibit artifacts that limit the benefits of this technique. An image restoration procedure employing a metric of defocus is proposed to remove these artifacts and therefore extend the DoF beyond the limit of conventional WC systems. A transmission optical microscope was designed and implemented to operate with WC. After suppression of partial coherence effects, the proposed image restoration method was successfully applied and extended DoF images are presented