Чувствительность напряжений по обводу контура фантома к изменениям комплексных сопротивлений неоднородностей в электроимпедансной томографии

Проведено аналіз впливу значень поверхневої провідності неоднорідностей на зміну значень комплексних напруг по обводу контуру фантома, порівняно з однорідним фантомом з комплексною поверхневою провідністю. Проведено аналіз впливу анізотропії скінченного елемента та всього фантома в цілому на обчислювані комплексні напруги. Отримано модель скінченного квадратного елемента, який складається з 1024х1024 скінченних елементів, отриманих по спрощеним наближеним формулам. Констатовано, що незалежно від різниці абсолютних значень напруг, виміряних при різних положеннях джерела струму, їх відносні прирощення залишаються незмінними. Для боротьби з такого роду анізотропією запропоновано разом з джерелом обертати сітки фантомів, як скінченних елементів, так і зон провідності. Остаточні висновки про межі чутливості вимірювальних пристроїв можна буде зробити лише після накопичення статистичних даних розв’язання зворотної задачі, обраним авторами методом зон провідності.Introduction. The analysis of surface conductivity influence of inhomogeneities to the changes of complex phantom contour voltages comparing with a homogeneous phantom with complex surface conductivity is carried out. The analysis of the influence of finite element and all phantom generally anisotropy on the calculated complex voltages is conducted. The results. The model of square finite element is obtained. It consists of 1024х1024 finite elements obtained by simplified approximate formulas. The relative increments of voltage values are unchanged regardless of the difference in the absolute values of voltages measured at different positions of the current source. It is proposed to rotate the phantom grids of finite elements and conductivity zones with the current source rotation to overcome this anisotropy. Conclusions. Final conclusions about the limits of the measuring device sensitivity could be done only after the data accumulation of solving the inverse problem by zones conductivity method.Проведен анализ влияния значений поверхностной проводимости неоднородностей на изменение значений комплексных напряжений по обводу контура фантома в сравнении с однородным фантомом с комплексной поверхностной проводимостью. Выполнен анализ влияния анизотропии конечного элемента и всего фантома в целом на вычисляемые комплексные напряжения. Получена модель конечного квадратного элемента, составленного из 1024х1024 конечных элементов, полученных по упрощенным формулам. Отмечено, что независимо от разницы абсолютных значений напряжений, вычисленных при различных положениях источника тока, их относительные приращения остаются неизменными. Для борьбы с такого рода анизотропией предложено вместе с источником поворачивать сетки фантомов, как конечных элементов, так и зон проводимости. Окончательные выводы о границах чувствительности измерительных устройств можно будет сделать после накопления статистических данных решения обратной задачи, выбранным авторами метода зон проводимости

Методи виявлення малорозмірних малорухомих цілей на фоні інтенсивного морського клатера

The search for methods of small targets detection by marine radars is carried out in this paper. As a result of the sources analysis three promising group of methods are selected – adaptive methods based on the clutter amplitude spherically invariant random process representation, methods based on non stohastic clutter representation and methods based on orthogonal transformations. Separately, neural networks and clutter polarization properties are noticed. Most of researches consider compound Gaussian distribution for high resolution sea clutter data modeling. Adaptive CFAR algoritm developed by Kelly for Gaussian clutter was extended for spherical invariant random process. This algorithm can detect target in compound Gaussian clutter and ensure CFAR detection if covariance matrix is precisely known. Many researches proposed different methods of covariance matrix estimating using information from cells adjacent with cell under test. Progress in this field is moving to finding less computation cost but more precise methods of covariance matrix estimation. Further progress for small slow moving targets in heavy sea clutter detection can develop in 3 ways – improving covariance matrix estimation, exploiting not stochastic methods and finding best orthogonal transform for sea clutter Doppler spectrum describing.В работе осуществляется поиск методов обнаружения малоразмерных малоподвижных целей морскими РЛС. В результате анализа источников избирается три перспективные группы методов – адаптивные методы, основанные на представлении амплитуды клатера сферически инвариантным случайным процессом, методы, основанные на моделирования клатера детерминированным процессом и методы, основанные на ортогональных преобразованиях. Отдельно отмечается использование нейронных сетей и поляризационных свойств клатера.В роботі здійснюється пошук методів виявлення малорозмірних малорухомих цілей морськими РЛС. В результаті аналізу джерел обирається три перспективні групи методів – адаптивні методи, основані на представленні амплітуди клатера сферично інваріантним випадковим процесом, методи, основані на представленні клатера детермінованим процесом, основані на ортогональних перетвореннях. Окремо відзначається використання нейронних мереж та поляризаційних властивостей клатера

Polarization techniques for mitigation of low grazing angle sea clutter

Maritime surveillance radars are critical in commerce, transportation, navigation, and defense. However, the sea environment is perhaps the most challenging of natural radar backdrops because maritime radars must contend with electromagnetic backscatter from the sea surface, or sea clutter. Sea clutter poses unique challenges in very low grazing angle geometries, where typical statistical assumptions regarding sea clutter backscatter do not hold. As a result, traditional constant false alarm rate (CFAR) detection schemes may yield a large number of false alarms while objects of interest may be challenging to detect. Solutions posed in the literature to date have been either computationally impractical or lacked robustness. This dissertation explores whether fully polarimetric radar offers a means of enhancing detection performance in low grazing angle sea clutter. To this end, MIT Lincoln Laboratory funded an experimental data collection using a fully polarimetric X-band radar assembled largely from commercial off-the-shelf components. The Point de Chene Dataset, collected on the Atlantic coast of Massachusetts’ Cape Ann in October 2015, comprises multiple sea states, bandwidths, and various objects of opportunity. The dataset also comprises three different polarimetric transmit schemes. In addition to discussing the radar, the dataset, and associated post-processing, this dissertation presents a derivation showing that an established multiple input, multiple output radar technique provides a novel means of simultaneous polarimetric scattering matrix measurement. A novel scheme for polarimetric radar calibration using a single active calibration target is also presented. Subsequent research leveraged this dataset to develop Polarimetric Co-location Layering (PCL), a practical algorithm for mitigation of low grazing angle sea clutter, which is the most significant contribution of this dissertation. PCL routinely achieves a significant reduction in the standard CFAR false alarm rate while maintaining detections on objects of interest. Moreover, PCL is elegant: It exploits fundamental characteristics of both sea clutter and object returns to determine which CFAR detections are due to sea clutter. We demonstrate that PCL is robust across a range of bandwidths, pulse repetition frequencies, and object types. Finally, we show that PCL integrates in parallel into the standard radar signal processing chain without incurring a computational time penalty

Automatic vessel monitoring with single and multidimensional SAR images in the wavelet domain

Spaceborne Synthetic Aperture Radar (SAR) techniques constitute an extremely promising alternative compared to traditional surveillance methods thanks to the all-weather and day-and-night capabilities of Radar linked with the large coverage of SAR images. Nowadays, the capabilities of satellite based SAR systems are confirmed by a wide amount of applications and experiments all over the world. Nevertheless, specific data exploitation methods are still to be developed to provide an efficient automatic interpretation of SAR data. The aim of this paper is to present an approach based on multiscale time–frequency analysis for the automatic detection of spots in a noisy background which is a critical matter in a number of SAR applications. The technique has been applied to automatic ship detection in single and multidimensional SAR imagery and it has proven to be a rapid, robust and reliable tool, able to manage complicated heterogeneous scenes where classical approaches may fail.Peer Reviewe

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

Wrecked: Impacts of Atlantic Tropical Cyclones on Neotropical Bird Migrants

North American birds are under pressure. With nearly 3 billion birds lost in the last half century (Rosenberg et al., 2019), understanding and quantifying incremental avian mortality is vital. There are many perils for birds that migrate from their breeding grounds in the mid and high latitudes of the continent to their wintering grounds in the Neotropics, including Atlantic tropical cyclones that birds can encounter as they cross through some of the busy hurricane corridors of the Gulf of Mexico, Caribbean, and western North Atlantic. The aim of my research is to provide the first comprehensive measure of the effect of Atlantic tropical cyclone activity on bird migration intensity by testing whether active hurricane seasons may cause a significant reduction in the number of Neotropical migrants. I used weather surveillance radars spanning the Gulf of Mexico to determine bird migration intensity from 1995 to 2018. I employed a dataset for which artificial intelligence processes separated avian targets from precipitation and background interference and that included an estimate of total seasonal bird passage. I determined the level of tropical cyclone activity from the Accumulated Cyclone Energy (ACE) index. To establish whether there was a relation between tropical cyclone activity and migrants, I used generalized additive mixed-effects models to test spring bird passage as a function of ACE during the peak of the fall migration season. I did this first with ACE extracted for the entire Atlantic basin and subsequently for subregions of the Atlantic (e.g., Gulf of Mexico and the Caribbean), and compared all models. I found a strong negative relationship between tropical cyclone activity in the Atlantic basin and the subsequent spring’s migrant bird passage. When there were more storms and/or stronger hurricanes across the whole North Atlantic Ocean during the peak of the fall migration between August and November, fewer birds returned the following spring. Specifically, there was a predicted decrease of 21.8% in the number of birds crossing the Gulf during springs following the most active Atlantic hurricane seasons compared to the least active seasons. While this finding might imply that storms directly impacted birds, a more granular examination of the data suggests other possibilities. The relationship between storm activity and spring bird passage was much weaker in the Gulf of Mexico and the Caribbean compared to the open Atlantic, even though more bird migration occurs in those areas. Instead, the negative association between storm activity and migration traffic may reflect a link between the short-term climatic variability that drives Atlantic hurricane seasons and (1) rainfall amounts in the wintering grounds, (2) winds across migration corridors, and/or (3) other environmental responses that impact birds’ survival. Knowing whether migrating birds are being killed or displaced by storms and/or other meteorological and climatic teleconnection patterns is especially important today in the face of further declines in North American bird abundance brought on by the expanding human footprint, rapid climate change, and more extreme weather events. An in-depth understanding of the impact of cyclones and/or related oceanic-atmospheric structures on bird migration could provide valuable insight into new subdisciplines and studies in aeroecology and meteorology

Radar and satellite observations of precipitation: space time variability, cross-validation, and fusion

2017 Fall.Includes bibliographical references.Rainfall estimation based on satellite measurements has proven to be very useful for various applications. A number of precipitation products at multiple time and space scales have been developed based on satellite observations. For example, the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center has developed a morphing technique (i.e., CMORPH) to produce global precipitation products by combining existing space-based observations and retrievals. The CMORPH products are derived using infrared (IR) brightness temperature information observed by geostationary satellites and passive microwave-(PMW) based precipitation retrievals from low earth orbit satellites. Although space-based precipitation products provide an excellent tool for regional, local, and global hydrologic and climate studies as well as improved situational awareness for operational forecasts, their accuracy is limited due to restrictions of spatial and temporal sampling and the applied parametric retrieval algorithms, particularly for light precipitation or extreme events such as heavy rain. In contrast, ground-based radar is an excellent tool for quantitative precipitation estimation (QPE) at finer space-time scales compared to satellites. This is especially true after the implementation of dual-polarization upgrades and further enhancement by urban scale X-band radar networks. As a result, ground radars are often critical for local scale rainfall estimation and for enabling forecasters to issue severe weather watches and warnings. Ground-based radars are also used for validation of various space measurements and products. In this study, a new S-band dual-polarization radar rainfall algorithm (DROPS2.0) is developed that can be applied to the National Weather Service (NWS) operational Weather Surveillance Radar-1988 Doppler (WSR-88DP) network. In addition, a real-time high-resolution QPE system is developed for the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) Dallas-Fort Worth (DFW) dense radar network, which is deployed for urban hydrometeorological applications via high-resolution observations of the lower atmosphere. The CASA/DFW QPE system is based on the combination of a standard WSR-88DP (i.e., KFWS radar) and a high-resolution dual-polarization X-band radar network. The specific radar rainfall methodologies at Sand X-band frequencies, as well as the fusion methodology merging radar observations at different temporal resolutions are investigated. Comparisons between rainfall products from the DFW radar network and rainfall measurements from rain gauges are conducted for a large number of precipitation events over several years of operation, demonstrating the excellent performance of this urban QPE system. The real-time DFW QPE products are extensively used for flood warning operations and hydrological modelling. The high-resolution DFW QPE products also serve as a reliable dataset for validation of Global Precipitation Measurement (GPM) satellite precipitation products. This study also introduces a machine learning-based data fusion system termed deep multi-layer perceptron (DMLP) to improve satellite-based precipitation estimation through incorporating ground radar-derived rainfall products. In particular, the CMORPH technique is applied first to derive combined PMW-based rainfall retrievals and IR data from multiple satellites. The combined PMW and IR data then serve as input to the proposed DMLP model. The high-quality rainfall products from ground radars are used as targets to train the DMLP model. In this dissertation, the prototype architecture of the DMLP model is detailed. The urban scale application over the DFW metroplex is presented. The DMLP-based rainfall products are evaluated using currently operational CMORPH products and surface rainfall measurements from gauge networks

Détection de bateaux dans les images de radar à ouverture synthétique

Le but principal de cette thèse est de développer des algorithmes efficaces et de concevoir un système pour la détection de bateaux dans les images Radar à Ouverture Synthetique (ROS.) Dans notre cas, la détection de bateaux implique en premier lieu la détection de cibles de points dans les images ROS. Ensuite, la détection d'un bateau proprement dit dépend des propriétés physiques du bateau lui-même, tel que sa taille, sa forme, sa structure, son orientation relative a la direction de regard du radar et les conditions générales de l'état de la mer. Notre stratégie est de détecter toutes les cibles de bateaux possibles dans les images de ROS, et ensuite de chercher autour de chaque candidat des évidences telle que les sillons. Les objectifs de notre recherche sont (1) d'améliorer 1'estimation des paramètres dans Ie modèle de distribution-K et de déterminer les conditions dans lesquelles un modèle alternatif (Ie Gamma, par exemple) devrait être utilise plutôt; (2) d'explorer Ie modèle PNN (Probabilistic Neural Network) comme une alternative aux modèles paramétriques actuellement utilises; (3) de concevoir un modèle de regroupement flou (FC : Fuzzy Clustering) capable de détecter les petites et grandes cibles de bateaux dans les images a un seul canal ou les images a multi-canaux; (4) de combiner la détection de sillons avec la détection de cibles de bateaux; (5) de concevoir un modèle de détection qui peut être utilisé aussi pour la détection des cibles de bateaux en zones costières.Abstract: The main purpose of this thesis is to develop efficient algorithms and design a system for ship detection from Synthetic Aperture Radar (SAR) imagery. Ship detection usually involves through detection of point targets on a radar clutter background.The detection of a ship depends on the physical properties of the ship itself, such as size, shape, and structure; its orientation relative to the radar look-direction; and the general condition of the sea state. Our strategy is to detect all possible ship targets in SAR images, and then search around each candidate for the wake as further evidence.The objectives of our research are (1) to improve estimation of the parameters in the K-distribution model and to determine the conditions in which an alternative model (Gamma, for example) should be used instead; (2) to explore a PNN (Probabilistic Neural Networks) model as an alternative to the commonly used parameteric models; (3) to design a FC (Fuzzy Clustering) model capable of detecting both small and large ship targets from single-channel images or multi-channel images; (4) to combine wake detection with ship target detection; (5) to design a detection model that can also be used to detect ship targets in coastal areas. We have developed algorithms for each of these objectives and integrated them into a system comprising six models.The system has been tested on a number of SAR images (SEASAT, ERS and RADARSAT-1, for example) and its performance has been assessed