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

Statistical Classification for Heterogeneous Polarimetric SAR Images

International audienceThis paper presents a general approach for high-resolution polarimetric SAR data classification in heterogeneous clutter, based on a statistical test of equality of covariance matrices. The Spherically Invariant Random Vector (SIRV) model is used to describe the clutter. Several distance measures, including classical ones used in standard classification methods, can be derived from the general test. The new approach provide a threshold over which pixels are rejected from the image, meaning they are not sufficiently "close" from any existing class. A distance measure using this general approach is derived and tested on a high-resolution polarimetric data set acquired by the ONERA RAMSES system. It is compared to the results of the classical decomposition and Wishart classifier under Gaussian and SIRV assumption. Results show that the new approach rejects all pixels from heterogeneous parts of the scene and classifies its Gaussian parts

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

Auto-regressive model based polarimetric adaptive detection scheme part I: Theoretical derivation and performance analysis

This paper deals with the problem of target detection in coherent radar systems exploiting polarimetric diversity. We resort to a parametric approach and we model the disturbance affecting the data as a multi-channel autoregressive (AR) process. Following this model, a new polarimetric adaptive detector is derived, which aims at improving the target detection capability while relaxing the requirements on the training data size and the computational burden with respect to existing solutions. A complete theoretical characterization of the asymptotic performance of the derived detector is provided, using two different target fluctuation models. The effectiveness of the proposed approach is shown against simulated data, in comparison with alternative existing solutions

Near-Surface Interface Detection for Coal Mining Applications Using Bispectral Features and GPR

The use of ground penetrating radar (GPR) for detecting the presence of near-surface interfaces is a scenario of special interest to the underground coal mining industry. The problem is difficult to solve in practice because the radar echo from the near-surface interface is often dominated by unwanted components such as antenna crosstalk and ringing, ground-bounce effects, clutter, and severe attenuation. These nuisance components are also highly sensitive to subtle variations in ground conditions, rendering the application of standard signal pre-processing techniques such as background subtraction largely ineffective in the unsupervised case. As a solution to this detection problem, we develop a novel pattern recognition-based algorithm which utilizes a neural network to classify features derived from the bispectrum of 1D early time radar data. The binary classifier is used to decide between two key cases, namely whether an interface is within, for example, 5 cm of the surface or not. This go/no-go detection capability is highly valuable for underground coal mining operations, such as longwall mining, where the need to leave a remnant coal section is essential for geological stability. The classifier was trained and tested using real GPR data with ground truth measurements. The real data was acquired from a testbed with coal-clay, coal-shale and shale-clay interfaces, which represents a test mine site. We show that, unlike traditional second order correlation based methods such as matched filtering which can fail even in known conditions, the new method reliably allows the detection of interfaces using GPR to be applied in the near-surface region. In this work, we are not addressing the problem of depth estimation, rather confining ourselves to detecting an interface within a particular depth range

Polarization Decomposition Algorithm for Detection Efficiency Enhancement

In the paper, a new polarization decomposition of the optimal detection algorithm in the partially homogeneous environment is presented. Firstly, the detectors Matched Subspace Detector (MSD) and Adaptive Subspace Detector (ASD) are adopted to deal with detection problems in the partially homogeneous environment. Secondly, the fitness function with polarization parameters is equivalently decomposed to enhance time detection efficiency in the algorithm. It makes the multiplication number of the fitness function from square to a linear increase along with the increase in parameters. Simulation results indicate that the proposed decomposition is much more efficient than direct use of the fitness function

Enhanced Microwave Imaging of the Subsurface for Humanitarian Demining Applications

© Cranfield University 2020. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright ownerThis thesis presents a theoretical analysis and applied evaluation deploying ground penetrat ing radar (GPR) for landmine detection. An original contribution has been made in designing and manufacturing a light-weight, low-cost, fully polarimetric antenna system for GPR, enabling easy transportation and as sembly. This facilitates extensive use by various smaller communities in remote areas. By achieving the goal of supplying various smaller communities with advanced ground pene trating radar technology the technological standard of landmine detection can be improved beyond existing solutions such as metal detection or manual probing. The novel radar system itself allows detection of various subsurface targets of different shapes and sizes, metallic and non-metallic, in a number of different soils, such as sand, loam or gravel and therefore can be used in versatile environments. The GPR system has been realised by designing novel light-weight, 3D printed X-band horn antennas, manufactured from single piece plastic then copper electroplated. These an tennas are 50% lighter than their commercial equivalents. They are incorporated in an an tenna array as a group of four to allow full-polarimetric imaging of the subsurface. High resolution images of landmines and calibration targets were performed in the subsurface over an experimental sand test bed. For performing subsurface measurements in the near-field, four novel gradient-index (GRIN) lenses were designed and 3D printed to be incorporated in the apertures of the X band antennas. The improved target detection from these lenses was proven by scanning the test bed and comparing the imaging data of the antenna array with and without lenses attached. A rigorous theoretical study of different decomposition techniques and their effect on the imaging and detection accuracy for polarimetric surface penetrating data was performed and applied to the gathered imaging data to reliably isolate and detect subsurface targets. Studied decomposition techniques were Pauli decomposition parameters and Yamaguchi polarime try decomposition. It was found that it is paramount to use both algorithms on one set of subsurface data to detect all features of a buried target. A novel temporal imaging technique was developed for exploiting natural occurring changes in soil moisture level, and hence its dielectric properties. Contrary to the previously intro duced imaging techniques this moisture change detection (MCD) mechanism does not rely on knowledge of the used measurement setup or deploying clutter suppression techniques. This time averaged technique uses several images of a moist subsurface taken over a period while the moisture evaporates from the soil. Each image pixel is weighted by the phase change occurring over the evaporation period and a resulting B-scan image reveals the subsurface targets without surrounding clutter. Finally, a multi-static antenna set-up is examined on its capability for suppressing sur face clutter and its limitations are verified by introducing artificial surface clutter in form of pebbles to the scene. The resulting technique was found to suppress up to 30 The GPR antenna system developed in this thesis and the corresponding imaging tech niques have contributed to a significant improvement in subsurface radar imaging perfor mance and target discrimination capabilities. This work will contribute to more efficient landmine clearance in some of the most challenged parts of the world.Ph

Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

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