143 research outputs found

    A Routine and Post-disaster Road Corridor Monitoring Framework for the Increased Resilience of Road Infrastructures

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    Static Background Removal in Vehicular Radar: Filtering in Azimuth-Elevation-Doppler Domain

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    A significant challenge in autonomous driving systems lies in image understanding within complex environments, particularly dense traffic scenarios. An effective solution to this challenge involves removing the background or static objects from the scene, so as to enhance the detection of moving targets as key component of improving overall system performance. In this paper, we present an efficient algorithm for background removal in automotive radar applications, specifically utilizing a frequency-modulated continuous wave (FMCW) radar. Our proposed algorithm follows a three-step approach, encompassing radar signal preprocessing, three-dimensional (3D) ego-motion estimation, and notch filter-based background removal in the azimuth-elevation-Doppler domain. To begin, we model the received signal of the FMCW multiple-input multiple-output (MIMO) radar and develop a signal processing framework for extracting four-dimensional (4D) point clouds. Subsequently, we introduce a robust 3D ego-motion estimation algorithm that accurately estimates radar ego-motion speed, accounting for Doppler ambiguity, by processing the point clouds. Additionally, our algorithm leverages the relationship between Doppler velocity, azimuth angle, elevation angle, and radar ego-motion speed to identify the spectrum belonging to background clutter. Subsequently, we employ notch filters to effectively filter out the background clutter. The performance of our algorithm is evaluated using both simulated data and extensive experiments with real-world data. The results demonstrate its effectiveness in efficiently removing background clutter and enhacing perception within complex environments. By offering a fast and computationally efficient solution, our approach effectively addresses challenges posed by non-homogeneous environments and real-time processing requirements

    Parametric Estimation Techniques for Space-Time Adaptive Processing with Applications for Airborne Bistatic Radar Systems

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    This thesis considers parametric scenario based methods for Space-Time Adaptive Processing (STAP) in airborne bistatic radar systems. STAP is a multidimensional filtering technique used to mitigate the influence of interference and noise in a target detector. To be able to perform the mitigation, an accurate estimate is required of the associated space-time covariance matrix to the interference and noise distribution. In an airborne bistatic radar system geometry-induced effects due to the bistatic configuration introduces variations in the angle-Doppler domain over the range dimension. As a consequence of this, clutter observations of such systems may not follow the same distribution over the range dimension. This phenomena may affect the estimator of the space-time covariance matrix.\ua0In this thesis, we study a parametric scenario based approach to alleviate the geometry-induced effects. Thus, the considered framework is based on so called radar scenarios. A radar scenario is a description of the current state of the bistatic configuration, and is thus dependent on a few parameters connected to the two radar platforms which comprise the configuration. The scenario description can via a parametric model be used to represent the geometry-induced effects present in the system. In the first topic of this thesis, an investigation is conducted of the effects on scenario parameter residuals on the performance of a detector. Moreover, two methods are presented which estimate unknown scenario parameters from secondary observations. In the first estimation method, a maximum likelihood estimate is calculated for the scenario parameters using the most recent set of secondary data. In the second estimation method, a density is formed by combination of the likelihood associated with the most recent set of radar observations with a prior density obtained by propagation of previously considered scenario parameter estimates through a dynamical model of the scenario platforms motion over time. From the formed density a maximum a posteriori estimate of the scenario parameters can be derived. Thus, in the second estimation method, the radar scenario is tracked over time. Consequently, in the first topic of the thesis, the sensitivity between scenario parameters and detector performance is evaluated in various aspects, and two methods are investigated to estimate unknown scenario parameters from different radar scenarios.\ua0In the second part of the thesis, the scenario description is used to estimate a space-time covariance matrix and to derive a generalized likelihood ratio test for the airborne bistatic radar configuration. Consequently, for the covariance matrix estimate, the scenario description is used to derive a transformation matrix framework which aims to limit the non-stationary behavior of the secondary data observed by a bistatic radar system. Using the scenario based transformation framework, a set of non-stationary secondary data can be transformed to become more stationarily distributed after the transformation. A transformed set of secondary data can then be used in a conventional estimator to estimate the space-time covariance matrix. Furthermore, as the scenario description provides a representation of the geometry-induced effects in a bistatic configuration, the scenario description can be used to incorporate these effects into the design of a detector. Thus, a generalized likelihood ratio test is derived for an airborne bistatic radar configuration. Moreover, the presented detector is adaptive towards the strength of both the clutter interference and the thermal noise

    Naval Postgraduate School Academic Catalog - February 2023

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    The inevitability of arbuscular mycorrhiza for sustainability in organic agriculture—A critical review

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    The arbuscular mycorrhizal fungi (AMF) are significant fertility-promoting microbes in soils. They enable soil fertility, soil-health and boost crop productivity. There are generalist and specialist groups among AMF in natural soils. Optimized use of specific AMF concerning crops and soils can improve agricultural sustainability. Thus, AMF is becoming an inevitable biological tool for improving crop productivity and soil health. Especially in the context of chemicalized agriculture undermining the sustainability of food security, safety, and human and ecosystem health, alternative agricultural means have become inevitable. Therefore, AMF has become essential in nature-friendly, organic agriculture. Of such farm fields, natural biological activity is enhanced to sustain soil fertility. Crops show increased innate immunity against pests and diseases in many such systems. Moreover, ecosystems remain healthy, and the soil is teeming with life in such farms. The primary goal of the review was a thorough critical analysis of the literature on AMF in organic agriculture to assess its efficiency as an ecotechnological tool in sustainable agricultural productivity. The novelty is that this is the first comprehensive review of literature on AMF concerning all aspects of organic agriculture. A vital systematic approach to the exhaustive literature collected using regular databases on the theme is followed for synthesizing the review. The review revealed the essentiality of utilizing specific mycorrhizal species, individually or in consortia, in diverse environmental settings to ensure sustainable organic crop production. However, for the exact usage of specific AMF in sustainable organic agriculture, extensive exploration of them in traditional pockets of specific crop cultivations of both chemical and organic fields and wild environments is required. Moreover, intensive experimentations are also necessary to assess them individually, in combinations, and associated with diverse beneficial soil bacteria

    Maritime Moving Target Detection, Tracking and Geocoding Using Range-Compressed Airborne Radar Data

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    Eine regelmäßige und großflächige überwachung des Schiffsverkehrs gewinnt zunehmend an Bedeutung, vor allem auch um maritime Gefahrenlagen und illegale Aktivitäten rechtzeitig zu erkennen. Heutzutage werden dafür überwiegend das automatische Identifikationssystem (AIS) und stationäre Radarstationen an den Küsten eingesetzt. Luft- und weltraumgestützte Radarsensoren, die unabhängig vom Wetter und Tageslicht Daten liefern, können die vorgenannten Systeme sehr gut ergänzen. So können sie beispielsweise Schiffe detektieren, die nicht mit AIS-Transpondern ausgestattet sind oder die sich außerhalb der Reichweite der stationären AIS- und Radarstationen befinden. Luftgestützte Radarsensoren ermöglichen eine quasi-kontinuierliche Beobachtung von räumlich begrenzten Gebieten. Im Gegensatz dazu bieten weltraumgestützte Radare eine große räumliche Abdeckung, haben aber den Nachteil einer geringeren temporalen Abdeckung. In dieser Dissertation wird ein umfassendes Konzept für die Verarbeitung von Radardaten für die Schiffsverkehr-überwachung mit luftgestützten Radarsensoren vorgestellt. Die Hauptkomponenten dieses Konzepts sind die Detektion, das Tracking, die Geokodierung, die Bildgebung und die Fusion mit AIS-Daten. Im Rahmen der Dissertation wurden neuartige Algorithmen für die ersten drei Komponenten entwickelt. Die Algorithmen sind so aufgebaut, dass sie sich prinzipiell für zukünftige Echtzeitanwendungen eignen, die eine Verarbeitung an Bord der Radarplattform erfordern. Darüber hinaus eignen sich die Algorithmen auch für beliebige, nicht-lineare Flugpfade der Radarplattform. Sie sind auch robust gegenüber Lagewinkeländerungen, die während der Datenerfassung aufgrund von Luftturbulenzen jederzeit auftreten können. Die für die Untersuchungen verwendeten Daten sind ausschließlich entfernungskomprimierte Radardaten. Da das Signal-Rausch-Verhältnis von Flugzeugradar-Daten im Allgemeinen sehr hoch ist, benötigen die neuentwickelten Algorithmen keine vollständig fokussierten Radarbilder. Dies reduziert die Gesamtverarbeitungszeit erheblich und ebnet den Weg für zukünftige Echtzeitanwendungen. Der entwickelte neuartige Schiffsdetektor arbeitet direkt im Entfernungs-Doppler-Bereich mit sehr kurzen kohärenten Verarbeitungsintervallen (CPIs) der entfernungskomprimierten Radardaten. Aufgrund der sehr kurzen CPIs werden die detektierten Ziele im Dopplerbereich fokussiert abgebildet. Wenn sich die Schiffe zusätzlich mit einer bestimmten Radialgeschwindigkeit bewegen, werden ihre Signale aus dem Clutter-Bereich hinausgeschoben. Dies erhöht das Verhältnis von Signal- zu Clutter-Energie und verbessert somit die Detektierbarkeit. Die Genauigkeit der Detektion hängt stark von der Qualität der von der Meeresoberfläche rückgestreuten Radardaten ab, die für die Schätzung der Clutter-Statistik verwendet werden. Diese wird benötigt, um einen Detektions-Schwellenwert für eine konstante Fehlalarmrate (CFAR) abzuleiten und die Anzahl der Fehlalarme niedrig zu halten. Daher umfasst der vorgeschlagene Detektor auch eine neuartige Methode zur automatischen Extraktion von Trainingsdaten für die Statistikschätzung sowie geeignete Ozean-Clutter-Modelle. Da es sich bei Schiffen um ausgedehnte Ziele handelt, die in hochauflösenden Radardaten mehr als eine Auflösungszelle belegen, werden nach der Detektion mehrere von einem Ziel stammende Pixel zu einem physischen Objekten zusammengefasst, das dann in aufeinanderfolgenden CPIs mit Hilfe eines Bewegungsmodells und eines neuen Mehrzielverfolgungs-Algorithmus (Multi-Target Tracking) getrackt wird. Während des Trackings werden falsche Zielspuren und Geisterzielspuren automatisch erkannt und durch ein leistungsfähiges datenbankbasiertes Track-Management-System terminiert. Die Zielspuren im Entfernungs-Doppler-Bereich werden geokodiert bzw. auf den Boden projiziert, nachdem die Einfallswinkel (DOA) aller Track-Punkte geschätzt wurden. Es werden verschiedene Methoden zur Schätzung der DOA-Winkel für ausgedehnte Ziele vorgeschlagen und anhand von echten Radardaten, die Signale von echten Schiffen beinhalten, bewertet

    Using a surface energy budget framework to characterize grass-biophysical response to changes in climate in support of on-farm decision making in Ireland

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    xxiv Abstract This thesis, for the first time in Ireland, uses a framework that combines a land surface scheme (LSS) based on a surface energy budget theory, available environmental observations, land surface and atmospheric analyses, to understand essential mechanistic factors that determine grass growth response across the Irish landscape. A soil moisture model parameter (C soil) is identified as the key factor that distinguishes soil types and their ability to retain water for plant growth, plant response to exchange processes, and drives the response of LSS in drying soils. A Modification of this parameter indicates that the LSS can be transferred to other locations. In the context of understanding the links between land surface dynamic processes and the persistence of 2018 summer drought regionally, drying soils and high atmospheric anomalies result in a reduced evapotranspiration (ET) process. This is the situation over grasslands in the east and south east of the country where a wet ‘evaporative’ regime quickly shifts into a ‘transitional’ regime in which vegetation functioning and ET are controlled by soil water availability. Particularly, a threshold value of soil moisture content that suggests the onset of 2018 agricultural drought has been found across the regions. The importance of water use efficiency for monitoring grass growth at field level and for distinguishing zones of optimum productivity is further discussed in the thesis. Overall, the findings demonstrate the potential consequences of climate change on Irish grasslands and the need for policies that are tailored to reinforcing observation networks to complement theories and model outputs akin to on-farm adaptation and optimization of water availability and productivity

    Biodiversity-Health-Sustainability Nexus in Socio-Ecological Production Landscapes and Seascapes (SEPLS)

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    This is an open access book. It is a compilation of case studies that provide useful knowledge and lessons that derive from on-the-ground activities and contribute to policy recommendations, focusing on the interlinkages between biodiversity and multiple dimensions of health (e.g., physical, mental, and spiritual) in managing socio-ecological production landscapes and seascapes (SEPLS). This book provides insights on how SEPLS approaches can contribute to more sustainable management of natural resources, achieving global biodiversity and sustainable development goals, and good health for all. It is also expected to offer useful knowledge and information for an upcoming three-year thematic assessment of “the interlinkages among biodiversity, water, food, and health” (the so-called “nexus assessment”) by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). The book begins with an introductory chapter followed by eleven case study chapters demonstrating the nexus between biodiversity, health, and sustainable development, and then a synthesis chapter clarifying the relevance of the case study findings to policy and academic discussions. It will be of interest to scholars, policymakers, and professionals in the field related to sustainable development
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