4,894 research outputs found
Subspace-based methodologies for the non-cooperative identification of aircraft by means of a synthetic database of radar signatures
Una de las principales preocupaciones dentro del mundo de la aviación es la identificación rápida, eficaz y fiable de cualquier objeto observado que se encuentre a cualquier distancia y bajo cualquier condición atmosférica. Gracias a los avances en tecnología radar, esto se ha conseguido. De hecho, los radares son los sensores más adecuados para el reconocimiento de blancos en vuelo ya que pueden operar en cualquier condición. El reconocimiento de blancos mediante radar es hoy un hecho, existiendo sistemas IFF (Identification Friend or Foe) capaces de comunicarse con una aeronave haciendo posible que ella misma se identifique por sí sola. Sin embargo, esta necesidad de comunicación directa puede ser un inconveniente en ciertos momentos. Así, aparecen las técnicas no cooperativas o NCTI (Non-Cooperative Target Identification), que no establecen ninguna comunicación con el blanco y normalmente hacen uso de radares de alta resolución. Éstos ven los blancos como compuestos por diversos puntos que dispersan la energía emitida por el radar, generando así una imagen de la reflectividad de un blanco, lo que se ha llamado su firma radar. Comparando dicha firma radar con una base de datos de firmas radar de blancos conocidos es posible establecer, mediante una serie de algoritmos de identificación, el tipo de blanco iluminado por el radar. Uno de los temas más cuestionados es cómo poblar y actualizar esta base de datos de firmas radar. De manera ideal, la base de datos debería de contener medidas de blancos reales en vuelo; desafortunadamente, la principal desventaja de esta estrategia radica en la dificultad de obtener firmas radar de aviones neutrales o enemigos. Por esta razón, esta tesis propone utilizar firmas radar de blancos ideales, generadas mediante simulaciones electromagnéticas, como base de datos. Con el avance de las herramientas de predicción electromagnética es posible obtener de manera rápida y a bajo coste firmas radar de cualquier blanco deseado y en cualquier orientación. De este modo, el principal objetivo de esta tesis yace en el desarrollo de algoritmos eficientes de identificación de aeronaves en vuelo de manera no cooperativa, con altas tasas de acierto y empleando una base de datos de blancos obtenida mediante simulación electromagnética. El escenario propuesto consiste en la comparación de firmas radar reales obtenidas en una campaña de medidas con una base de datos compuesta por firmas radar simuladas, con ello se pretende por un lado, simular un escenario más realista, en el que las firmas de los blancos recogidas por el radar no tienen porqué tener la misma calidad que aquellas de la base de datos y por otro, comprobar que la identificación de un avión real mediante simulaciones es posible
Towards joint communication and sensing (Chapter 4)
Localization of user equipment (UE) in mobile communication networks has been supported from the early stages of 3rd generation partnership project (3GPP). With 5th Generation (5G) and its target use cases, localization is increasingly gaining importance. Integrated sensing and localization in 6th Generation (6G) networks promise the introduction of more efficient networks and compelling applications to be developed
Quantitative Integration of Multiple Near-Surface Geophysical Techniques for Improved Subsurface Imaging and Reducing Uncertainty in Discrete Anomaly Detection
Currently there is no systematic quantitative methodology in place for the integration of two or more coincident data sets collected using near-surface geophysical techniques. As the need for this type of methodology increases—particularly in the fields of archaeological prospecting, UXO detection, landmine detection, environmental site characterization/remediation monitoring, and forensics—a detailed and refined approach is necessary. The objective of this dissertation is to investigate quantitative techniques for integrating multi-tool near-surface geophysical data to improve subsurface imaging and reduce uncertainty in discrete anomaly detection. This objective is fulfilled by: (1) correlating multi-tool geophysical data with existing well-characterized “targets”; (2) developing methods for quantitatively merging different geophysical data sets; (3) implementing statistical tools within Statistical Analysis System (SAS) to evaluate the multiple integration methodologies; and (4) testing these new methods at several well-characterized sites with varied targets (i.e., case studies). Three geophysical techniques utilized in this research are: ground penetrating radar (GPR), electromagnetic (ground conductivity) methods (EM), and magnetic gradiometry. Computer simulations are developed to generate synthetic data with expected parameters such as heterogeneity of the subsurface, type of target, and spatial sampling. The synthetic data sets are integrated using the same methodologies employed on the case-study sites to (a) further develop the necessary quantitative assessment scheme, and (b) determine if these merged data sets do in fact yield improved results. A controlled setting within The University of Tennessee Geophysical Research Station permits the data (and associated anomalous bodies) to be spatially correlated with the locations of known targets. Error analysis is then conducted to guide any modifications to the data integration methodologies before transitioning to study sites of unknown subsurface features. Statistical analysis utilizing SAS is conducted to quantitatively evaluate the effectiveness of the data integration methodologies and determine if there are significant improvements in subsurface imaging, thus resulting in a reduction in the uncertainty of discrete anomaly detection
A Hybrid Templated-Based Composite Classification System
An automatic target classification system contains a classifier which reads a feature as an input and outputs a class label. Typically, the feature is a vector of real numbers. Other features can be non-numeric, such as a string of symbols or alphabets. One method of improving the performance of an automatic classification system is through combining two or more independent classifiers that are complementary in nature. Complementary classifiers are observed by finding an optimal method for partitioning the problem space. For example, the individual classifiers may operate to identify specific objects. Another method may be to use classifiers that operate on different features. We propose a design for a hybrid composite classification system, which exploits both real-numbered and non-numeric features with a template matching classification scheme. This composite classification system is made up of two independent classification systems.These two independent classification systems, which receive input from two separate sensors are then combined over various fusion methods for the purpose of target identification. By using these two separate classifiers, we explore conditions that allow the two techniques to be complementary in nature, thus improving the overall performance of the classification system. We examine various fusion techniques, in search of the technique that generates the best results. We investigate different parameter spaces and fusion rules on example problems to demonstrate our classification system. Our examples consider various application areas to help further demonstrate the utility of our classifier. Optimal classifier performance is obtained using a mathematical framework, which takes into account decision variables based on decision-maker preferences and/or engineering specifications, depending upon the classification problem at hand
Advanced signal processing techniques for WiFi-based Passive Radar for short-range surveillance
In this work, advanced signal processing techniques for a Passive Radar (PR) based on WiFi transmissions are considered. The possibility to exploit such a ubiquitous and accessible source is shown to be an appropriate choice for the detection, localization and imaging of vehicles, people and aircrafts within short ranges in both outdoor and indoor environments
The MASSIMO system for the safeguarding of historic buildings in a seismic area: operationally-oriented platforms
In this paper, the non-invasive system MASSIMO is presented for the monitoring and the seismic vulnerability mitigation of the cultural heritage. It integrates ground-based, airborne and space-borne remote sensing tools with geophysical and in situ surveys to provide the multi-spatial (regional, urban and building scales) and multi-temporal (long-term, short-term, near-real-time and real-time scales) monitoring of test areas and buildings. The measurements are integrated through web-based GIS and 3D visual platforms to support decision-making stakeholders involved in urban planning and structural requalification. An application of this system is presented over the Calabria region for the town of Cosenza and a test historical complex
ANALYZING THE LIFE-CYCLE OF UNSTABLE SLOPES USING APPLIED REMOTE SENSING WITHIN AN ASSET MANAGEMENT FRAMEWORK
An asset management framework provides a methodology for monitoring and maintaining assets, which include anthropogenic infrastructure (e.g., dams, embankments, and retaining structures) and natural geological features (e.g., soil and rock slopes). It is imperative that these assets operate efficiently, effectively, safely, and at a high standard since many assets are located along transportation corridors (highways, railways, and waterways) and can cause severe damage if compromised. Assets built on or around regions prone to natural hazards are at an increased risk of deterioration and failure. The objective of this study is to utilize remote sensing techniques such as InSAR, LiDAR, and optical photogrammetry to identify assets, assess past and current conditions, and perform long-term monitoring in transportation corridors and urbanized areas prone to natural hazards. Provided are examples of remote sensing techniques successfully applied to various asset management procedures: the characterization of rock slopes (Chapter 2), identification of potentially hazardous slopes along a railroad corridor (Chapter 3), monitoring subsidence rates of buildings in San Pedro, California (Chapter 4), and mapping displacement rates on dams in India (Chapter 5) and California (Chapter 6). A demonstration of how InSAR can be used to map slow landslides (those with a displacement rate \u3c 16 mm/year and may be undetectable without sensitive instrumentation) and update the California Landslide Inventory on the Palos Verdes Peninsula is provided in Chapter 7. Long-term landslide monitoring using optical photogrammetry, GPS, and InSAR measurements is also used to map landslide activity at three orders of magnitude (meter to millimeter scales) in Chapter 8. Remote sensing has proven to be an effective tool at measuring ground deformation, which is an implicit indicator of how geotechnical asset condition changes (e.g., deteriorates) over time. Incorporating these techniques into a geotechnical asset management framework will provide greater spatial and temporal data for preventative approaches towards natural hazards
Aspects of Synthetic Vision Display Systems and the Best Practices of the NASA's SVS Project
NASA s Synthetic Vision Systems (SVS) Project conducted research aimed at eliminating visibility-induced errors and low visibility conditions as causal factors in civil aircraft accidents while enabling the operational benefits of clear day flight operations regardless of actual outside visibility. SVS takes advantage of many enabling technologies to achieve this capability including, for example, the Global Positioning System (GPS), data links, radar, imaging sensors, geospatial databases, advanced display media and three dimensional video graphics processors. Integration of these technologies to achieve the SVS concept provides pilots with high-integrity information that improves situational awareness with respect to terrain, obstacles, traffic, and flight path. This paper attempts to emphasize the system aspects of SVS - true systems, rather than just terrain on a flight display - and to document from an historical viewpoint many of the best practices that evolved during the SVS Project from the perspective of some of the NASA researchers most heavily involved in its execution. The Integrated SVS Concepts are envisagements of what production-grade Synthetic Vision systems might, or perhaps should, be in order to provide the desired functional capabilities that eliminate low visibility as a causal factor to accidents and enable clear-day operational benefits regardless of visibility conditions
Advanced Data Mining and Machine Learning Algorithms for Integrated Computer-Based Analyses of Big Environmental Databases
Einsicht in die räumliche Verteilung geotechnischer und hydrologischer Untergrundeigenschaften sowie von Reservoir- und Umweltparametern sind grundlegend für geowissenschaftliche Forschungen. Entwicklungen in den Bereichen geophysikalische Erkundung sowie Fernerkundung resultieren in der Verfügbarkeit verschiedenster Verfahren für die nichtinvasive, räumlich kontinuierliche Datenerfassung im Rahmen hochauflösender Messverfahren. In dieser Arbeit habe ich verschiedene Verfahren für die Analyse erdwissenschaftlicher Datenbasen entwickelt auf der Basis von Wissenserschließungsverfahren. Eine wichtige Datenbasis stellt geophysikalische Tomographie dar, die als einziges geowissenschaftliches Erkundungsverfahren 2D und 3D Abbilder des Untergrunds liefern kann. Mittels unterschiedlicher Verfahren aus den Bereichen intelligente Datenanalyse und maschinelles Lernen (z.B. Merkmalsextraktion, künstliche neuronale Netzwerke, etc.) habe ich ein Verfahren zur Datenanalyse mittels künstlicher neuronaler Netzwerke entwickelt, das die räumlich kontinuierliche 2D oder 3D Vorhersage von lediglich an wenigen Punkten gemessenen Untergrundeigenschaften im Rahmen von Wahrscheinlichkeitsaussagen ermöglicht. Das Vorhersageverfahren basiert auf geophysikalischer Tomographie und berücksichtigt die Mehrdeutigkeit der tomographischen Bildgebung. Außerdem wird auch die Messunsicherheit bei der Erfassung der Untergrundeigenschaften an wenigen Punkten in der Vorhersage berücksichtigt. Des Weiteren habe ich untersucht, ob aus den Trainingsergebnissen künstlicher neuronaler Netzwerke bei der Vorhersage auch Aussagen über die Realitätsnähe mathematisch gleichwertiger Lösungen der geophysikalischen tomographischen Bildgebung abgeleitet werden können. Vorhersageverfahren wie das von mir vorgeschlagene, können maßgeblich zur verbesserten Lösung hydrologischer und geotechnischer Fragestellungen beitragen.
Ein weiteres wichtiges Problem ist die Kartierung der Erdoberfläche, die von grundlegender Bedeutung für die Bearbeitung verschiedener ökonomischer und ökologischer Fragestellungen ist, wie z.B., die Identifizierung von Lagerstätten, den Schutz von Böden, oder Ökosystemmanagement. Kartierungsdaten resultieren entweder aus technischen (objektiven) Messungen oder visuellen (subjektiven) Untersuchungen durch erfahrene Experten. Im Rahmen dieser Arbeit zeige ich erste Entwicklungen hin zu einer automatisierten und schnellen Integration technischer und visueller (subjektiver) Daten auf der Basis unterschiedlicher intelligenter Datenanalyseverfahren (z.B., Graphenanalyse, automatische Konturerfassung, Clusteranalyse, etc.). Mit solchem Verfahren sollen hart oder weich klassifizierte Karten erstellt werden, die das Untersuchungsgebiet optimal segmentieren um höchstmögliche Konformität mit allen verfügbaren Daten zu erzielen
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