Theory and Design of a Highly Compressed Dropped-Channel Polarimetric Synthetic Aperture Radar

Compressed sensing (CS) is a recent mathematical technique that leverages the sparsity in certain sets of data to solve an underdetermined system and recover a full set of data from a sub-Nyquist set of measurements of the data. Given the size and sparsity of the data, radar has been a natural choice to apply compressed sensing to, typically in the fast-time and slow-time domains. Polarimetric synthetic aperture radar (PolSAR) generates a particularly large amount of data for a given scene; however, the data tends to be sparse. Recently a technique was developed to recover a dropped PolSAR channel by leveraging antenna crosstalk information and using compressed sensing. In this dissertation, we build upon the initial concept of the dropped-channel PolSAR CS in three ways. First, we determine a metric which relates the measurement matrix to the l2 recovery error. The new metric is necessary given the deterministic nature of the measurement matrix. We then determine a range of antenna crosstalk required to recover a dropped PolSAR channel. Second, we propose a new antenna design that incorporates the relatively high levels of crosstalk required by a dropped-channel PolSAR system. Finally, we integrate fast- and slow-time compression schemes into the dropped-channel model in order to leverage sparsity in additional PolSAR domains and overall increase the compression ratio. The completion of these research tasks has allowed a more accurate description of a PolSAR system that compresses in fast-time, slow-time, and polarization; termed herein as highly compressed PolSAR. The description of a highly compressed PolSAR system is a big step towards the development of prototype hardware in the future

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

Front-end receiver for miniaturised ultrasound imaging

Point of care ultrasonography has been the focus of extensive research over the past few decades. Miniaturised, wireless systems have been envisaged for new application areas, such as capsule endoscopy, implantable ultrasound and wearable ultrasound. The hardware constraints of such small-scale systems are severe, and tradeoffs between power consumption, size, data bandwidth and cost must be carefully balanced. To address these challenges, two synthetic aperture receiver architectures are proposed and compared. The architectures target highly miniaturised, low cost, B-mode ultrasound imaging systems. The first architecture utilises quadrature (I/Q) sampling to minimise the signal bandwidth and computational load. Synthetic aperture beamforming is carried out using a single-channel, pipelined protocol in order to minimise system complexity and power consumption. A digital beamformer dynamically apodises and focuses the data by interpolating and applying complex phase rotations to the I/Q samples. The beamformer is implemented on a Spartan-6 FPGA and consumes 296mW for a frame rate of 7Hz. The second architecture employs compressive sensing within the finite rate of innovation (FRI) framework to further reduce the data bandwidth. Signals are sampled below the Nyquist frequency, and then transmitted to a digital back-end processor, which reconstructs I/Q components non-linearly, and then carries out synthetic aperture beamforming. Both architectures were tested in hardware using a single-channel analogue front-end (AFE) that was designed and fabricated in AMS 0.35μm CMOS. The AFE demodulates RF ultrasound signals sequentially into I/Q components, and comprises a low-noise preamplifier, mixer, programmable gain amplifier (PGA) and lowpass filter. A variable gain low noise preamplifier topology is used to enable quasi-exponential time-gain control (TGC). The PGA enables digital selection of three gain values (15dB, 22dB and 25.5dB). The bandwidth of the lowpass filter is also selectable between 1.85MHz, 510kHz and 195kHz to allow for testing of both architectural frameworks. The entire AFE consumes 7.8 mW and occupies an area of 1.5×1.5 mm. In addition to the AFE, this thesis also presents the design of a pseudodifferential, log-domain multiplier-filter or “multer” which demodulates low-RF signals in the current-domain. This circuit targets high impedance transducers such as capacitive micromachined ultrasound transducers (CMUTs) and offers a 20dB improvement in dynamic range over the voltage-mode AFE. The bandwidth is also electronically tunable. The circuit was implemented in 0.35μm BiCMOS and was simulated in Cadence; however, no fabrication results were obtained for this circuit. B-mode images were obtained for both architectures. The quadrature SAB method yields a higher image SNR and 9% lower root mean squared error with respect to the RF-beamformed reference image than the compressive SAB method. Thus, while both architectures achieve a significant reduction in sampling rate, system complexity and area, the quadrature SAB method achieves better image quality. Future work may involve the addition of multiple receiver channels and the development of an integrated system-on-chip.Open Acces

Noncontact Vital Signs Detection

Human health condition can be accessed by measurement of vital signs, i.e., respiratory rate (RR), heart rate (HR), blood oxygen level, temperature and blood pressure. Due to drawbacks of contact sensors in measurement, non-contact sensors such as imaging photoplethysmogram (IPPG) and Doppler radar system have been proposed for cardiorespiratory rates detection by researchers.The UWB pulse Doppler radars provide high resolution range-time-frequency information. It is bestowed with advantages of low transmitted power, through-wall capabilities, and high resolution in localization. However, the poor signal to noise ratio (SNR) makes it challenging for UWB radar systems to accurately detect the heartbeat of a subject. To solve the problem, phased-methods have been proposed to extract the phase variations in the reflected pulses modulated by human tiny thorax motions. Advance signal processing method, i.e., state space method, can not only be used to enhance SNR of human vital signs detection, but also enable the micro-Doppler trajectories extraction of walking subject from UWB radar data.Stepped Frequency Continuous Wave (SFCW) radar is an alternative technique useful to remotely monitor human subject activities. Compared with UWB pulse radar, it relieves the stress on requirement of high sampling rate analog-to-digital converter (ADC) and possesses higher signal-to-noise-ratio (SNR) in vital signs detection. However, conventional SFCW radar suffers from long data acquisition time to step over many frequencies. To solve this problem, multi-channel SFCW radar has been proposed to step through different frequency bandwidths simultaneously. Compressed sensing (CS) can further reduce the data acquisition time by randomly stepping through 20% of the original frequency steps.In this work, SFCW system is implemented with low cost, off-the-shelf surface mount components to make the radar sensors portable. Experimental results collected from both pulse and SFCW radar systems have been validated with commercial contact sensors and satisfactory results are shown

Smart Sensor Networks For Sensor-Neural Interface

One in every fifty Americans suffers from paralysis, and approximately 23% of paralysis cases are caused by spinal cord injury. To help the spinal cord injured gain functionality of their paralyzed or lost body parts, a sensor-neural-actuator system is commonly used. The system includes: 1) sensor nodes, 2) a central control unit, 3) the neural-computer interface and 4) actuators. This thesis focuses on a sensor-neural interface and presents the research related to circuits for the sensor-neural interface. In Chapter 2, three sensor designs are discussed, including a compressive sampling image sensor, an optical force sensor and a passive scattering force sensor. Chapter 3 discusses the design of the analog front-end circuit for the wireless sensor network system. A low-noise low-power analog front-end circuit in 0.5μm CMOS technology, a 12-bit 1MS/s successive approximation register (SAR) analog-to-digital converter (ADC) in 0.18μm CMOS process and a 6-bit asynchronous level-crossing ADC realized in 0.18μm CMOS process are presented. Chapter 4 shows the design of a low-power impulse-radio ultra-wide-band (IR-UWB) transceiver (TRx) that operates at a data rate of up to 10Mbps, with a power consumption of 4.9pJ/bit transmitted for the transmitter and 1.12nJ/bit received for the receiver. In Chapter 5, a wireless fully event-driven electrogoniometer is presented. The electrogoniometer is implemented using a pair of ultra-wide band (UWB) wireless smart sensor nodes interfacing with low power 3-axis accelerometers. The two smart sensor nodes are configured into a master node and a slave node, respectively. An experimental scenario data analysis shows higher than 90% reduction of the total data throughput using the proposed fully event-driven electrogoniometer to measure joint angle movements when compared with a synchronous Nyquist-rate sampling system. The main contribution of this thesis includes: 1) the sensor designs that emphasize power efficiency and data throughput efficiency; 2) the fully event-driven wireless sensor network system design that minimizes data throughput and optimizes power consumption

Compressive Sensing for Microwave and Millimeter-Wave Array Imaging

PhDCompressive Sensing (CS) is a recently proposed signal processing technique that has already found many applications in microwave and millimeter-wave imaging. CS theory guarantees that sparse or compressible signals can be recovered from far fewer measure- ments than those were traditionally thought necessary. This property coincides with the goal of personnel surveillance imaging whose priority is to reduce the scanning time as much as possible. Therefore, this thesis investigates the implementation of CS techniques in personnel surveillance imaging systems with different array configurations. The first key contribution is the comparative study of CS methods in a switched array imaging system. Specific attention has been paid to situations where the array element spacing does not satisfy the Nyquist criterion due to physical limitations. CS methods are divided into the Fourier transform based CS (FT-CS) method that relies on conventional FT and the direct CS (D-CS) method that directly utilizes classic CS formulations. The performance of the two CS methods is compared with the conventional FT method in terms of resolution, computational complexity, robustness to noise and under-sampling. Particularly, the resolving power of the two CS methods is studied under various cir- cumstances. Both numerical and experimental results demonstrate the superiority of CS methods. The FT-CS and D-CS methods are complementary techniques that can be used together for optimized efficiency and image reconstruction. The second contribution is a novel 3-D compressive phased array imaging algorithm based on a more general forward model that takes antenna factors into consideration. Imaging results in both range and cross-range dimensions show better performance than the conventional FT method. Furthermore, suggestions on how to design the sensing con- figurations for better CS reconstruction results are provided based on coherence analysis. This work further considers the near-field imaging with a near-field focusing technique integrated into the CS framework. Simulation results show better robustness against noise and interfering targets from the background. The third contribution presents the effects of array configurations on the performance of the D-CS method. Compressive MIMO array imaging is first derived and demonstrated with a cross-shaped MIMO array. The switched array, MIMO array and phased array are then investigated together under the compressive imaging framework. All three methods have similar resolution due to the same effective aperture. As an alternative scheme for the switched array, the MIMO array is able to achieve comparable performance with far fewer antenna elements. While all three array configurations are capable of imaging with sub-Nyquist element spacing, the phased array is more sensitive to this element spacing factor. Nevertheless, the phased array configuration achieves the best robustness against noise at the cost of higher computational complexity. The final contribution is the design of a novel low-cost beam-steering imaging system using a flat Luneburg lens. The idea is to use a switched array at the focal plane of the Luneburg lens to control the beam-steering. By sequentially exciting each element, the lens forms directive beams to scan the region of interest. The adoption of CS for image reconstruction enables high resolution and also data under-sampling. Numerical simulations based on mechanically scanned data are conducted to verify the proposed imaging system.China Scholarship Council Engineering and Physical Sciences Research Council (EPSRC) funding (EP/I034548/1)