Passive radar detects and ranges targets by receiving signals which are reflected off targets. Communication transmissions are generally used, however, theoretically any signal with a suitable ambiguity function may be used. The exploitation of an existing transmitter and the removal of emissions allow passive radars to act as a complementary sensor which is useful in environments where conventional active radar is not well suited. Such environments are in covert operations and in situations where a low cost or spectrally efficient solution is required. Most developed passive radars employ intensive signal processing and use application specific equipment to achieve detection. The high-end processors and receiver equipment, however, detract from some of the inherent advantages in the passive radar architecture. These include the lower cost and power requirements achieved by removing transmitter hardware. This study investigates the challenges faced when removing application-specific and high end components from the system and replacing them with low-cost alternatives. Solutions to these challenges are presented and validated by designing and evaluating a radar using these principles. It was found that the major limitation in passive radar is the dynamic range of the receiver. While processing the signals was, and is, a significant challenge, be implemented on a low-cost, low-power embedded processor. This was achieved by asserting a few limitations to the configuration, exploiting the subsequently generated redundancy, and taking advantage of the parallelism by using general purpose graphics processing.. Even on this processor, the system was able to run in real time and able to detect targets up to 91 km (bistatic range of 195 km) from the radar. / Dissertation (MEng)--University of Pretoria, 2016. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted

Commercial concealed weapon detection systems are large and expensive and are not suitable to be used as a portable system. Currently, new methods of concealed weapon detection are being developed to build small and compact systems. One such method is based upon the natural resonances of objects; however, no such system has made it to the market due to the low quality of the signals used in the detection algorithms. In this thesis, a prototype concealed weapon detection system is developed and tested for operation in a cluttered environment. This system utilizes the late-time portion of a radar return to extract the resonance information of an unknown target. After proper signal processing and clutter suppression, the signals are classified to determine if the object is a threat. Multiple measurements with frequency-sweep and time-domain systems are used to verify the algorithm. Microwave tissue imaging techniques aim to reconstruct the internal dielectric distribution of the tissue and rely on the dielectric contrast between healthy and malignant tissues. This contrast has been shown to be weak, and therefore, the signals are easily susceptible to noise. This thesis proposes and validates a method for signal-to-noise ratio analysis of complex S-parameter data sets that are used for microwave imaging. A study of de-noising and artifact reduction techniques for microwave holographic imaging is also presented. / Thesis / Master of Applied Science (MASc)

Augmented reality (AR) is seeing a rapid expansion into several domains due to the proliferation of more accessible and powerful hardware. While augmented reality user interfaces (AR UIs) allow the presentation of information atop the real world, this extra visual data potentially comes at a cost of increasing the visual clutter of the users' field of view, which can increase visual search time, error rates, and have an overall negative effect on performance. Visual clutter has been studied for existing display technologies, but there are no established measures of visual clutter for AR UIs which precludes the study of the effects of clutter on performance in AR UIs. The first objective of this research is to determine the applicability of extant image analysis measures of feature congestion, edge density, and sub-band entropy for measuring visual clutter in the head-worn optical see-through AR space and establish a relationship between image analysis measures of clutter and visual search time. These image analysis measures are specifically chosen to quantify clutter, as they can be applied to complex and naturalistic scenes, as is common to experience while using an optical see-through AR UI. The second objective is to examine the effects of AR UIs comprised of multiple apparent depths on user performance through the metric of visual search time. The third objective is to determine the effects of other AR UI properties such as target clutter, target eccentricity, target apparent depth and target total distance on performance as measured through visual search time. These results will then be used to develop a visual clutter score, which will rate different AR UIs against each other. Image analysis measures for clutter of feature congestion, edge density, and sub-band entropy of clutter were correlated to visual search time when they were taken for the overall AR UI and when they were taken for a target object that a participant was searching for. In the case of an AR UI comprised of both projected and AR parts, image analysis measures were not correlated to visual search time for the constituent AR UI parts (projected or AR) but were still correlated to the overall AR UI clutter. Target eccentricity also had an effect on visual search time, while target apparent depth and target total distance from center did not. Target type and AR object percentage also had an effect on visual search time. These results were synthesized into a general model known as the "AR UI Visual Clutter Score Algorithm" using a multiple regression. This model can be used to compare different AR UIs to each other in order to identify the AR UI that is projected to have lower target visual search times. / Doctor of Philosophy / Augmented reality is a novel but growing technology. The ability to project visual information into the real-world comes with many benefits, but at the cost of increasing visual clutter. Visual clutter in existing displays has been shown to negatively affect visual search time, error rates, and general performance, but there are no established measures of visual clutter augmented reality displays, so it is unknown if visual clutter will have the same effects. The first objective of this research is to establish measures of visual clutter for augmented reality displays. The second objective is to better understand the unique properties of augmented reality displays, and how that may affect ease of use. Measures of visual clutter were correlated to visual search time when they were taken for the augmented reality user interface, and when they were taken for a given target object within that a participant was searching for. It was also found that as targets got farther from the center of the field of view, visual search time increased, while the depth of a target from the user and the total distance a target was from the user did not. Study 1 also showed that target type and AR object percentage also had an effect on visual search time. Combining these results gives a model that can be used to compare different augmented reality user interfaces to each other.

Durante las últimas décadas se ha realizado mucho esfuerzo para encontrar soluciones eficientes al problema de detección de los radares marítimos modernos. La probabilidad de detección de objetos de interés depende altamente de los esquemas de detección seleccionados y sus distribuciones de clutter y señal. Se han desarrollado diferentes estrategias de detección con el objetivo de discriminar el retorno marino de la señal de interés. Estas estrategias establecen un umbral que depende de la potencia del retorno marino local basada en un test de hipótesis para una determinada falsa alarma. En esta tesis, se presentan algunos detectores radar con el propósito de encontrar soluciones eficientes al problema de discriminar entre los objetos de interés y el ambiente marino, especialmente para el caso de condiciones no homogéneas que usualmente acaecen en el escenario marino. Con ese propósito, se describen algunos modelos del entorno marino por medio de sus distribuciones estadísticas para baja y alta resolución. Por ejemplo, para el caso de baja resolución y ángulos de incidencia altos, el clutter generalmente se modela por medio de la distribución Gaussiana. Sin embargo, para el caso de radares modernos de alta resolución, donde las celdas de rango son pequeñas con muy pocos dispersores, el retorno radar se aleja de las distribuciones Gaussianas y el ambiente marino se representa muy bien mediante distribuciones de cola larga, tales como las distribuciones Weibull y K. En este trabajo, se discuten los principios básicos de la detección radar. Se realiza una descripción detallada de algunos esquemas clásicos, tales como el detector de Neyman Pearson, el GLRT, y algunos esquemas de detección, como el CA CFAR, el GO CFAR, el SO CFAR, el OS CFAR y el detector propuesto por [60] . Teniendo en cuenta que la detección radar se puede interpretar como un problema de reconocimiento de patrones, las redes neuronales son apropiadas para tratarlo, por consiguiente, se consideraron algunas soluciones por medio de varios algoritmos tales como el ANN CFAR, el CANN CFAR y el NNCAOS CFAR. Finalmente, se presenta un nuevo detector radar en el contexto de los sistemas no-coherentes que operan en ambientes marinos no homogéneos. Se considera el caso especial de un radar de alta resolución con un ángulo de incidencia bajo, que funciona en condiciones de mar adversas. El esquema de detección que se plantea, se basa en la propuesta de un nuevo modelo del ambiente marino. Los objetivos de diseño de esta distribución fueron introducir grados de libertad adicionales (parámetros) con respecto a la distribución K para modelar los casos de colas pesadas y también obtener un detector en forma cerrada que aproxime la propiedad CFAR cuando trabaja en condiciones extremas. / During the last decades much effort has been done to find efficient solutions to the problem of detection for modern maritime radar. The probability of detection of a target highly depends on the selected detection scheme and its signal and clutter distributions. Different detection strategies have been developed in order to discriminate the clutter from the signal of interest. These detection strategies set a threshold that depends on the local clutter power based on a hypothesis test for an expected false alarm rate. In this thesis, several radar detectors were introduced with the purpose of finding efficient solutions to the problem of discriminating between targets and the clutter environment, especially for the case of non-homogeneous situations that usually happen in the sea scenario. To that purpose some clutter models are described by means of their statistical distributions for low and high resolution. For example, for the case of low resolution and high grazing angles the clutter is generally modeled by means of the Gaussian distribution. However, for the case of the modern high resolution radars, where the range cells are small with only few scatterers, the clutter models depart from the Gaussian distributions and the sea environments are well represented by means of long tailed distributions, like the Weibull and the K. In this work, the basic radar detection principles are discussed. A detailed description of some classic detection schemes are given, like the Neyman Pearson Detector, the GLRT, and some CFAR detection schemes, like the CA CFAR, the GO CFAR, the SO CFAR, the OS CFAR and the detector proposed by [60] . Considering that the radar detection may be interpreted as a pattern recognition, the Neural Networks are suitable to deal with the problem. Some solutions were considered by means of several algorithms like the specialized ANN CFAR, the CANN CFAR and the NNCAOS CFAR. Finally, a new radar detector in the context of non-coherent radar systems operating under non-homogeneous sea clutter and the special case of a high resolution radar at low grazing angles under adverse sea conditions, is presented. The novel detection scheme is based on a new clutter distribution model. The design objectives of this clutter distribution were to introduce additional degrees of freedom (parameters) with respect to the K distribution to model heavy tailed cases and also to obtain a closed-form detector that approximates the CFAR property when working under very stringent detection conditions.

Submitted by Jos? Henrique Henrique (jose.neves@ufvjm.edu.br) on 2017-06-06T21:08:54Z No. of bitstreams: 2 jose_lucas_camargos.pdf: 1772896 bytes, checksum: ed61126d3964190579d1888b21edf3aa (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Rodrigo Martins Cruz (rodrigo.cruz@ufvjm.edu.br) on 2017-06-22T15:30:03Z (GMT) No. of bitstreams: 2 jose_lucas_camargos.pdf: 1772896 bytes, checksum: ed61126d3964190579d1888b21edf3aa (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-06-22T15:30:03Z (GMT). No. of bitstreams: 2 jose_lucas_camargos.pdf: 1772896 bytes, checksum: ed61126d3964190579d1888b21edf3aa (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES) / Funda??o de Amparo ? Pesquisa do Estado de Minas Gerais (FAPEMIG) / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico (CNPq) / O objetivo deste trabalho foi avaliar a influ?ncia da redu??o de parcelas em algumas medi??es de invent?rio florestal cont?nuo na modelagem do crescimento e produ??o de um povoamento de Eucalyptus sp. O modelo de Clutter foi utilizado na modelagem do crescimento e da produ??o florestal, a partir de dados selecionados de 374 parcelas distribu?das em 172 talh?es. Foram utilizadas tr?s alternativas de modelagem: sem estratifica??o do povoamento, com estratifica??o do povoamento e um conjunto de 30 simula??es. Estas simula??es foram amostras de 40 parcelas aleat?rias cada, sem estratifica??o, com o objetivo de possibilitar uma an?lise geral dos efeitos do fen?meno estudado. Em todas as alternativas, foi realizada a modelagem do crescimento e da produ??o sem considerar redu??es nas medi??es de invent?rio florestal cont?nuo e considerando tr?s situa??es de redu??o. As situa??es consideradas foram medi??es de 25%, 50% e 75% das parcelas do invent?rio florestal cont?nuo nas medi??es dois e quatro, e 100% nas medi??es um, tr?s e cinco. Esta mesma metodologia foi repetida, por?m com a utiliza??o do modelo de regress?o n?o linear Log?stico, acrescido de um fator de corre??o. Ambos os modelos apresentaram boa precis?o para a predi??o e proje??o do volume do povoamento em idades futuras. O modelo Log?stico com fator de corre??o, entretanto, foi mais preciso que o modelo de Clutter. Para ambos os modelos foi constatado que n?o houveram perdas significativas de precis?o acarretadas pelas redu??es no n?mero de parcelas em algumas medi??es do invent?rio florestal cont?nuo. Para a modelagem do crescimento e da produ??o, ? recomendado que estas redu??es sejam criteriosas e que preferencialmente a ?ltima medi??o n?o seja reduzida. / Disserta??o (Mestrado) ? Programa de P?s-Gradua??o em Ci?ncia Florestal, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2017. / This study had as object to evaluate the influence of the reduction of plots at some measurements of continuous forest inventory in the growth and yield modeling of a Eucalyptus sp. forest stand. The Clutter model was used in forest growth and yield modeling, using selected data of 374 distributed plots in 172 forest compartments. It were used three modeling alternatives: without stratification of the forest stand, with stratification of the forest stand and a set of 30 simulations. These simulations were samples of 40 plots each one, without stratification, objectifying a general analysis about the studied phenomenon. To all the alternatives, it was made the forest growth and yield modeling without considering reductions in continuous forest inventory measurements and considering three reduction situations. The considered situations were measurements of 25%, 50% and 75% of the continuous forest inventory plots in the measures two and four, and 100% in the measures one, three and five. This same methodology was repeated, but using the Logistic nonlinear regression model, plus a correction factor. Both models had good precision to predicting and projecting the forest stand volume at future ages. The Logistic model with a correcting factor, however, had best precision than the Clutter model. For both models it was found that there was not significant losses of accuracy caused by reductions in the number of plots at some continuous forest inventory measurements. To forest growth and yield modeling, it is recommended for these reductions to be judicious and preferably the last measurement to not be reduced.

No description available.

In this study, novel methods for high-resolution radar target detection in non-Gaussian clutter environment are proposed. In solution of the problem, two approaches are used: Non-coherent detection that operates on the envelope-detected signal for thresholding and coherent detection that performs clutter suppression, Doppler processing and thresholding at the same time. The proposed non-coherent detectors, which are designed to operate in non-Gaussian and range-heterogeneous clutter, yield higher performance than the conventional methods that were designed either for Gaussian clutter or heterogeneous clutter. The proposed coherent detector exploits the information in all the range cells and pulses and performs the clutter reduction and thresholding simultaneously. The design is performed for uncorrelated, partially correlated and fully correlated clutter among range cells. The performance analysis indicates the superiority of the designed methods over the classical ones, in fully correlated and partially correlated situations. In addition, by design of detectors for multiple targets and making corrections to the conventional methods, the target-masking problem of the classical detectors is alleviated.

Under a number of practical operating scenarios, traditional moving target indicator (MTI) systems inadequately suppress ground clutter in airborne radar systems. Due to the moving platform, the clutter gains a nonzero relative velocity and spreads the power across Doppler frequencies. This obfuscates slow-moving targets of interest near the "direct current" component of the spectrum. In response, space-time adaptive processing (STAP) techniques have been developed that simultaneously operate in the space and time dimensions for effective clutter cancellation. STAP algorithms commonly operate under the assumption of homogeneous clutter, where the returns are described by complex, white Gaussian distributions. Empirical evidence shows that this assumption is invalid for many radar systems of interest, including high-resolution radar and radars operating at low grazing angles. We are interested in these heterogeneous cases, i.e., cases when the Gaussian model no longer suffices. Hence, the development of reliable STAP algorithms for real systems depends on the accuracy of the heterogeneous clutter models. The clutter of interest in this work includes heterogeneous texture clutter and point clutter. We have developed a cell-based clutter model (CCM) that provides simple, yet faithful means to simulate clutter scenarios for algorithm testing. The scene generated by the CMM can be tuned with two parameters, essentially describing the spikiness of the clutter scene. In one extreme, the texture resembles point clutter, generating strong returns from localized range-azimuth bins. On the other hand, our model can also simulate a flat, homogeneous environment. We prove the importance of model-based STAP techniques, namely knowledge-aided parametric covariance estimation (KAPE), in filtering a gamut of heterogeneous texture scenes. We demonstrate that the efficacy of KAPE does not diminish in the presence of typical spiky clutter. Computational complexities and susceptibility to modeling errors prohibit the use of KAPE in real systems. The computational complexity is a major concern, as the standard KAPE algorithm requires the inversion of an MNxMN matrix for each range bin, where M and N are the number of array elements and the number of pulses of the radar system, respectively. We developed a Gram Schmidt (GS) KAPE method that circumvents the need of a direct inversion and reduces the number of required power estimates. Another unavoidable concern is the performance degradations arising from uncalibrated array errors. This problem is exacerbated in KAPE, as it is a model-based technique; mismatched element amplitudes and phase errors amount to a modeling mismatch. We have developed the power-ridge aligning (PRA) calibration technique, a novel iterative gradient descent algorithm that outperforms current methods. We demonstrate the vast improvements attained using a combination of GS KAPE and PRA over the standard KAPE algorithm under various clutter scenarios in the presence of array errors.

No description available.

Visual clutter denotes a disordered collection of graphical entities in information visualization. It can obscure the structure present in the data. Even in a small dataset, visual clutter makes it hard for the viewer to find patterns, relationships and structure. In this thesis, I study visual clutter with four distinct visualization techniques, and present the concept and framework of Clutter-Based Dimension Reordering (CBDR). Dimension order is an attribute that can significantly affect a visualization's expressiveness. By varying the dimension order in a display, it is possible to reduce clutter without reducing data content or modifying the data in any way. Clutter reduction is a display-dependent task. In this thesis, I apply the CBDR framework to four different visualization techniques. For each display technique, I determine what constitutes clutter in terms of display properties, then design a metric to measure visual clutter in this display. Finally I search for an order that minimizes the clutter in a display. Different algorithms for the searching process are discussed in this thesis as well. In order to gather users' responses toward the clutter measures used in the Clutter-Based Dimension Reordering process and validate the usefulness of CBDR, I also conducted an evaluation with two groups of users. The study result proves that users find our approach to be helpful for visually exploring datasets. The users also had many comments and suggestions for the CBDR approach as well as for visual clutter reduction in general. The content and result of the user study are included in this thesis.