Representation of Radar Micro-Dopplers Using Customized Dictionaries

Human motions give rise to frequency modulations, known as micro-Dopplers, to continuous wave radar signals. Micro-Doppler signals have been extensively researched for the classification of different types of human motions as well as to distinguish humans from other moving targets. However, there are two main scenarios where the performance of existing algorithms deteriorates significantly—one, when the channel consists of multiple moving targets resulting in distorted signatures, and two, when the systems conditions during the training stage deviate significantly from the conditions during the test stage. In this chapter, it is demonstrated that both of these limitations can be overcome by representing the radar data through customized dictionaries, fine-tuned to provide sparser representations of the data, than traditional data-independent dictionaries such as Fourier or wavelets. The performances of the algorithms are evaluated with both simulated and measured radar data gathered from moving humans in indoor line-of-sight conditions

Master of Science

thesisNondestructive evaluation (NDE) is a means of assessing the reliability and integrity of a structural component and provides such information as the presence, location, extent, and type of damage in the component. Structural health monitoring (SHM) is a subfield of NDE, and focuses on a continuous monitoring of a structure while in use. SHM has been applied to structures such as bridges, buildings, pipelines, and airplanes with the goal of detecting the presence of damage as a means of determining whether a structure is in need of maintenance. SHM can be posed as a modeling problem, where an accurate model allows for a more reliable prediction of structural behavior. More reliable predictions make it easier to determine if something is out of the ordinary with the structure. Structural models can be designed using analytical or empirical approaches. Most SHM applications use purely analytical models based on finite element analysis and fundamental wave propagation equations to construct behavioral predictions. Purely empirical models exist, but are less common. These often utilize pattern recognition algorithms to recognize features that indicate damage. This thesis uses a method related to the k-means algorithm known as dictionary learning to train a wave propagation model from full wavefield data. These data are gathered from thin metal plates that exhibit complex wavefields dominated by multipath interference. We evaluate our model for its ability to detect damage in structures on which the model was not trained. These structures are similar to the training structure, but variable in material type and thickness. This evaluation will demonstrate how well learned dictionaries can both detect damage in a complex wavefield with multipath interference, and how well the learned model generalizes to structures with slight variations in properties. The damage detection and generalization results achieved using this empirical model are compared to similar results using both an analytical model and a support vector machine model

Sparsity-Driven Micro-Doppler Feature Extraction for Dynamic Hand Gesture Recognition

In this paper, a sparsity-driven method of micro-Doppler analysis is proposed for dynamic hand gesture recognition with radar sensors. First, sparse representations of the echoes reflected from dynamic hand gestures are achieved through the Gaussian-windowed Fourier dictionary. Second, the micro-Doppler features of dynamic hand gestures are extracted using the orthogonal matching pursuit algorithm. Finally, the nearest neighbor classifier is combined with the modified Hausdorff distance to recognize dynamic hand gestures based on the sparse micro-Doppler features. Experiments with real radar data show that the recognition accuracy produced by the proposed method exceeds 96% under moderate noise, and the proposed method outperforms the approaches based on principal component analysis and deep convolutional neural network with small training dataset

Measuring blood flow and pro-inflammatory changes in the rabbit aorta

Atherosclerosis is a chronic inflammatory disease that develops as a consequence of progressive entrapment of low density lipoprotein, fibrous proteins and inflammatory cells in the arterial intima. Once triggered, a myriad of inflammatory and atherogenic factors mediate disease progression. However, the role of pro-inflammatory activity in the initiation of atherogenesis and its relation to altered mechanical stresses acting on the arterial wall is unclear. Estimation of wall shear stress (WSS) and the inflammatory mediator NF-κB is consequently useful. In this thesis novel ultrasound tools for accurate measurement of spatiotemporally varying 2D and 3D blood flow, with and without the use of contrast agents, have been developed. This allowed for the first time accurate, broad-view quantification of WSS around branches of the rabbit abdominal aorta. A thorough review of the evidence for a relationship between flow, NF-κB and disease was performed which highlighted discrepancies in the current literature and was used to guide the study design. Subsequently, methods for the measurement and colocalization of the spatial distribution of NF-κB, arterial permeability and nuclear morphology in the aorta of New Zealand White rabbits were developed. It was demonstrated that endothelial pro-inflammatory changes are spatially correlated with patterns of WSS, nuclear morphology and arterial permeability in vivo in the rabbit descending and abdominal aorta. The data are consistent with a causal chain between WSS, macromolecule uptake, inflammation and disease, and with the hypothesis that lipids are deposited first, through flow-mediated naturally occurring transmigration that, in excessive amounts, leads to subsequent inflammation and disease.Open Acces

Pre-Trained Driving in Localized Surroundings with Semantic Radar Information and Machine Learning

Entlang der Signalverarbeitungskette von Radar Detektionen bis zur Fahrzeugansteuerung, diskutiert diese Arbeit eine semantischen Radar Segmentierung, einen darauf aufbauenden Radar SLAM, sowie eine im Verbund realisierte autonome Parkfunktion. Die Radarsegmentierung der (statischen) Umgebung wird durch ein Radar-spezifisches neuronales Netzwerk RadarNet erreicht. Diese Segmentierung ermöglicht die Entwicklung des semantischen Radar Graph-SLAM SERALOC. Auf der Grundlage der semantischen Radar SLAM Karte wird eine beispielhafte autonome Parkfunktionalität in einem realen Versuchsträger umgesetzt. Entlang eines aufgezeichneten Referenzfades parkt die Funktion ausschließlich auf Basis der Radar Wahrnehmung mit bisher unerreichter Positioniergenauigkeit. Im ersten Schritt wird ein Datensatz von 8.2 · 10^6 punktweise semantisch gelabelten Radarpunktwolken über eine Strecke von 2507.35m generiert. Es sind keine vergleichbaren Datensätze dieser Annotationsebene und Radarspezifikation öffentlich verfügbar. Das überwachte Training der semantischen Segmentierung RadarNet erreicht 28.97% mIoU auf sechs Klassen. Außerdem wird ein automatisiertes Radar-Labeling-Framework SeRaLF vorgestellt, welches das Radarlabeling multimodal mittels Referenzkameras und LiDAR unterstützt. Für die kohärente Kartierung wird ein Radarsignal-Vorfilter auf der Grundlage einer Aktivierungskarte entworfen, welcher Rauschen und andere dynamische Mehrwegreflektionen unterdrückt. Ein speziell für Radar angepasstes Graph-SLAM-Frontend mit Radar-Odometrie Kanten zwischen Teil-Karten und semantisch separater NDT Registrierung setzt die vorgefilterten semantischen Radarscans zu einer konsistenten metrischen Karte zusammen. Die Kartierungsgenauigkeit und die Datenassoziation werden somit erhöht und der erste semantische Radar Graph-SLAM für beliebige statische Umgebungen realisiert. Integriert in ein reales Testfahrzeug, wird das Zusammenspiel der live RadarNet Segmentierung und des semantischen Radar Graph-SLAM anhand einer rein Radar-basierten autonomen Parkfunktionalität evaluiert. Im Durchschnitt über 42 autonome Parkmanöver (∅3.73 km/h) bei durchschnittlicher Manöverlänge von ∅172.75m wird ein Median absoluter Posenfehler von 0.235m und End-Posenfehler von 0.2443m erreicht, der vergleichbare Radar-Lokalisierungsergebnisse um ≈ 50% übertrifft. Die Kartengenauigkeit von veränderlichen, neukartierten Orten über eine Kartierungsdistanz von ∅165m ergibt eine ≈ 56%-ige Kartenkonsistenz bei einer Abweichung von ∅0.163m. Für das autonome Parken wurde ein gegebener Trajektorienplaner und Regleransatz verwendet

Advanced Sensing and Image Processing Techniques for Healthcare Applications

This Special Issue aims to attract the latest research and findings in the design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificially intelligent devices and algorithms to assist/monitor the elderly, patients, and the disabled population