Pattern theory, a mathematical framework for representing knowledge of complex patterns developed by applied mathematician Ulf Grenander, has been shown to have potential uses in automatic target recognition (ATR). Prior research performed in the mid-1990s at Washington University in St. Louis resulted in ATR algorithms based on concepts in pattern theory for forward-looking infrared (FLIR) and laser radar (LADAR) imagery, but additional work was needed to create algorithms that could be implemented in real ATR systems. This was due to performance barriers and a lack of calibration between target models and real data. This work addresses some of these issues by exploring techniques that can be used to create practical pattern-theoretic ATR algorithms. This dissertation starts by reviewing the previous pattern-theoretic ATR research described above and discussing new results involving the unification of two previously separate outcomes of that research: multi-target detection/recognition and thermal state estimation in FLIR imagery. To improve the overall utility of pattern-theoretic ATR, the following areas are re-examined: 1) generalized diffusion processes to update target pose estimates and 2) the calibration of thermal models with FLIR target data. The final section of this dissertation analyzes the fundamental accuracy limits of target pose estimation under different sensor conditions, independent of the target detection/recognition algorithm employed. The Cramér-Rao lower bound (CRLB) is used to determine these accuracy limits.

This thesis concerns detection and recognition of ground object using data from laser radar systems. Typical ground objects are vehicles and land mines. For these objects, the orientation and articulation are unknown. The objects are placed in natural or urban areas where the background is unstructured and complex. The performance of laser radar systems is analyzed, to achieve models of the uncertainties in laser radar data. A ground object recognition method is presented. It handles general, noisy 3D point cloud data. The approach is based on the fact that man-made objects on a large scale can be considered be of rectangular shape or can be decomposed to a set of rectangles. Several approaches to rectangle fitting are presented and evaluated in Monte Carlo simulations. There are error-in-variables present and thus, geometric fitting is used. The objects can have parts that are subject to articulation. A modular least squares method with outlier rejection, that can handle articulated objects, is proposed. This method falls within the iterative closest point framework. Recognition when several similar models are available is discussed. The recognition method is applied in a query-based multi-sensor system. The system covers the process from sensor data to the user interface, i.e., from low level image processing to high level situation analysis. In object detection and recognition based on laser radar data, the range value’s accuracy is important. A general direct-detection laser radar system applicable for hard-target measurements is modeled. Three time-of-flight estimation algorithms are analyzed; peak detection, constant fraction detection, and matched filter. The statistical distribution of uncertainties in time-of-flight range estimations is determined. The detection performance for various shape conditions and signal-tonoise ratios are analyzed. Those results are used to model the properties of the range estimation error. The detector’s performances are compared with the Cramér-Rao lower bound. The performance of a tool for synthetic generation of scanning laser radar data is evaluated. In the measurement system model, it is possible to add several design parameters, which makes it possible to test an estimation scheme under different types of system design. A parametric method, based on measurement error regression, that estimates an object’s size and orientation is described. Validations of both the measurement system model and the measurement error model, with respect to the Cramér-Rao lower bound, are presented.

Ingeniero Civil Eléctrico / En la Astronomía ha habido un salto cuantitativo gigantesco desde el nacimiento de la tecnología CCD y las imágenes digitales. A pesar de ello, todavía existe un espacio de mejora en lo que respecta a las técnicas para estimación de parámetros importantes que caracterizan a las estrellas. Es por eso que esta Memoria de Título se presenta como objetivo el estudiar y cuantificar el uso de nuevos enfoques de estimación modernas no aplicados aún en esta disciplina para la estimación de la posición de objetos luminosos (Astrometría). Para poder entender el problema se presenta qué es una cámara digital y su uso en la astronomía, especificamente en la astrometría, además de presentar importantes conceptos astronómicos que se usan a lo largo de la memoria, como lo son el Point Spread Function y el Full Width at Half Maximum. Por otro lado, se da un repaso a los elementos de estimación necesarios para resolver el problema, como Cramér-Rao, Cramér-Rao Bayesiano y los estimadores Esperanza Condicional, Maximum Likelihood y Least Squares. La implementación del estimador se realizará a partir de una formalización completa del problema de estimación en astrometría, donde se incluirá también el trabajo de los algoritmos necesarios para encontrar el valor numérico tanto del estimador como de su error cuadrático medio. Se mostrará también la resolución de la cota de Cramér-Rao, tanto para la versión paramétrica como la bayesiana. Se hace un análisis de las herramientras presentadas usando como figura de mérito el MSE (Error Cuadrático Medio). A partir de ello, se muestra cómo varía este valor como función del tamaño del pixel, la relación de señal-ruido y sus ganancias relativas, para posteriormente estudiar las diferencias entre la Cota Bayesiana de Cramér-Rao y el MSE de la Esperanza Condicional, el estimador propuesto para el problema. Finalmente se concluye, viendo que existen ganancias significativas del enfoque Bayesiano en regímenes de baja relación señal-ruido y gran tamaño de pixel. Además se verifica que la cota Bayesiana de Cramér-Rao es un buen predictor del MSE de la Esperanza Condicional, lo cual trae nuevas preguntas que se pueden plantear como trabajo futuro a partir de esta Memoria de Título.

A Multiple-Input Multiple Output (MIMO) radar can be broadly defined as a radar system employing multiple transmit waveforms and having the ability to jointly process signals received at multiple receive antennas. In terms of configurations, the antennas can be widely separated or co-located. The first part of the thesis is on detection under Gaussian and non-Gaussian environments using a MIMO radar which contains several widely separated subarrays with one or more elements each. Two different situations are considered. Firstly, we consider that the interference is Gaussian but correlation between subarrays can arise due to insufficient spacing and the imperfect orthogonality of waveforms. Secondly, we consider that the interference is non-Gaussian, a situation which arises under sea and ground clutter and when the resolution is very high. The second part is on the application of MIMO techniques to Space-Time Adaptive Processing (STAP). The coherent MIMO configuration is studied in terms of antenna element distribution and inter-element spacing to improve detection and estimation performance. A preliminary study is also done on the use of spatial diversity to improve detection stability w.r.t. target Radar Cross Section (RCS) fluctuations and velocity direction changes.

ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California / A broadband passive direction finding system utilizing Luneburg lens has been investigated. With the simulated power level distribution at the detectors mounted on a Luneburg lens, both Cramér-Rao bound (CRB) and the root mean square error (RMS) based on the Correlation Algorithm (CA) for the direction of arrival (DoA) estimation have been derived and calculated. Guidelines on how to design the Luneburg lens detecting system have been studied. Finally, as a proof-of-concept demonstration, the DoA performance of a Luneburg lens fabricated using the polymer jetting technology with five detectors 10° equally spaced to receive the azimuth signal from -20° to 20° is demonstrated.

Les méthodes de trajectographie conventionnelles par mesures d'angle supposent que la source est en mouvement rectiligne uniforme tandis que l'observateur est manœuvrant. Dans cette thèse, nous remettons en cause cette hypothèse en proposant un autre modèle de cinématique de la source : le mouvement circulaire uniforme. Nous prouvons qu'une telle trajectoire est observable à partir d'un observateur en mouvement rectiligne uniforme. Puis, nous étudions l'apport de mesures additionnelles de fréquence ou la faisabilité de la trajectographie par mesures de distances. Le cas d'une source en mouvement rectiligne uniforme et d'un observateur manœuvrant est étudié pour ce dernier type de mesures. Chaque cas donne lieu à une analyse de l'observabilité de la trajectoire de la source et à la mise au point de l'estimateur du maximum de vraisemblance. Nous montrons que ce dernier s'avère le plus souvent efficace.

L'émergence des formes d'onde numériques en radar et l'engouement de la communauté scientifique pour leur versatilité éprouvée en télécom, soulèvent naturellement chez les ingénieurs radaristes la question de l'amélioration effective des performances opérationnelles par ces nouvelles formes d'onde, notamment en matière de haute-résolution. Les travaux publiés sur le sujet sont prometteurs, à ceci près qu'ils sont le plus souvent basés sur des modèles théoriques un peu éloignés de la réalité opérationnelle ou sur des scénarios simplistes relativement à la capacité haute résolution envisagée (par exemple le faible nombre de sources pris en compte). En effet la prise en compte d'un modèle d'observation réaliste (large bande, à fréquence d'échantillonnage élevée) et de scénario à grand nombre de contributeurs conduit à des estimateurs dont la complexité d'implémentation n'est pas compatible des puissances de calcul actuelles. Une approche alternative, et compatible des puissances de calcul actuelles, pour la qualification des performances haute résolution est l'utilisation des bornes inférieures d'estimation, principalement la borne de Cramèr-Rao déterministe. L'examen de la littérature courante (notamment les monographies de référence) sur la borne de Cramèr-Rao déterministe a fait apparaître des lacunes relatives à sa formulation dans le contexte radar qui nous intéresse, à savoir MIMO large bande, multisources, multiparamètres à observations multiples. En effet dans la littérature courante, les observations multiples sont définies comme des réalisations multiples indépendantes d'un même modèle d'observation, alors qu'en radar il s'agit en général de la combinaison de modèles d'observation différents (variation de la forme d'onde). Ce constat a motivé l'essentiel de ce travail, à savoir l'établissement d'une expression analytique générale de la borne de Cramèr-Rao déterministe MIMO large bande, multisources, multiparamètres à modèles d'observations multiples pour la qualification (asymptotique) des performances en estimation d'un radar actif. Ce travail fournit un outil de comparaison des performances haute-résolution des différentes formes d'onde, dont les nouvelles formes d'onde numériques. De façon générale, l'expression analytique générale de la borne de Cramèr-Rao obtenue fournit la base théorique pour le développement des futurs radars à haute résolution.

This work addresses two primary topics related to vibrations in structures. The first topic is the use of a spatially distributed sensor network for localization of vibration events. I use a received signal strength (RSS) framework that presumes exponential energy decay with distance to the source. I derive the Cramér-Rao bound (CRB) for this parameter estimation problem, with the unknown parameters being source location, source intensity, and the energy dissipation rate. In this framework, I show that the CRB matches the variance of maximum likelihood estimators (MLEs) in more computationally expensive Monte Carlo trials. I also compare the CRB to the results of physical experiments to test the power of the CRB to predict spatial areas where MLEs show practical evidence of being ill-conditioned. Supported by this evidence, I recommend the CRB as a simple measure of localization accuracy, which may be used to optimize sensor layouts before installation. I demonstrate how this numerical optimization may be performed for some regions of interest with simple geometries. The second topic investigates modal vibrations of multi-body structures built from simple one-dimensional elements, with networks of elastic strings as the primary example. I introduce a method of using a nonlinear eigenvalue problem (NLEVP) to express boundary conditions of the vibrating elements so that the (infinitely many) eigenvalues of the full structure are the eigenvalues of the finite-dimensional NLEVP. The mode shapes of the structure can then be recovered in analytic form (not as a discretization) from the corresponding eigenvectors of the NLEVP. I show some advantages of this method over dynamic stiffness matrices, which is another NLEVP framework for modal analysis. In numerical experiments, I test several contour integration solvers for NLEVPs on sample problems generated from string networks. / Doctor of Philosophy / This work deals with two primary topics related to vibrations in structures. The first topic is the use of vibration sensors to detect movement or impact and to estimate the location of the detected event. Sensors that are close to the event will record a larger amount of energy than the sensors that are farther away, so comparing the signals of several sensors can approximately establish the event location. In this way, vibration sensors might be used to monitor activity in a building without the use of intrusive cameras. The accuracy of location estimates can be greatly affected by the relative positions of the sensors and the event. Generally, location estimates tend to be most accurate if the sensors closely surround the event, and less accurate if the event is outside of the sensor zone. These principles are useful, but not precise. Given a framework for how event energy and noise are picked up by the sensors, the Cramér-Rao bound (CRB) is a formula for the achievable accuracy of location estimates. I demonstrate that the CRB is usefully similar to the location estimate accuracy from experimental data collected from a volunteer walking through a sensor-rigged hallway. I then show how CRB computations may be used to find an optimal arrangement of sensors. The match between the CRB and the accuracy of the experiments suggests that the sensor layout that optimizes the CRB will also provide accurate location estimates in a real building. The other main topic is how the vibrations of a structure can be understood through the structure's natural vibration frequencies and corresponding vibration shapes, called the "modes" of the structure. I connect vibration modes to the abstract framework of "nonlinear eigenvalue problems" (NLEVPs). An NLEVP is a square matrix-valued function for which one wants to find the inputs that make the matrix singular. But these singular matrices are usually isolated---% distributed among the infinitely many matrices of the NLEVP in places that are difficult to predict. After discussing NLEVPs in general and some methods for solving them, I show how the vibration modes of certain structures can be represented by the solutions of NLEVPs. The structures I analyze are multi-body structures that are made of simple interconnected pieces, such as elastic strings strung together into a spider web. Once a multi-body structure has been cast into the NLEVP form, an NLEVP solver can be used to find the vibration modes. Finally, I demonstrate that this method can be computationally faster than many traditional modal analysis techniques.

Estimating parameters is the problem of finding their values from measurements and modelling. Parameters describe properties of a system; material, for instance, are defined by mechanical, electrical, and chemical parameters. Fisher information is an information measure, giving information about how changes in the parameter effect the estimation. The Fisher information includes the physical model of the problem and the statistical model of noise. The Cramér-Rao bound is the inverse of the Fisher information and gives the best possible variance for any unbiased estimator. This thesis considers aspects of sensitivity analysis in two applied material parameter estimation problems. Sensitivity analysis with the Fisher information and the Cramér-Rao bound is used as a tool for evaluation of measurement feasibilities, comparison of measurement set-ups, and as a quantitative measure of the trade-off between accuracy and resolution in inverse imaging. The first application is with estimation of the wood grain angle parameter in trees and logs. The grain angle is the angle between the direction of the wood fibres and the direction of growth; a large grain angle strongly correlates to twist in sawn timber. In the thesis, measurements with microwaves are argued as a fast and robust measurement technique and electromagnetic modelling is applied, exploiting the anisotropic properties of wood. Both two-dimensional and three-dimensional modelling is considered. Mathematical modelling is essential, lowering the complexity and speeding up the computations. According to a sensitivity analysis with the Cramér-Rao bound, estimation of the wood grain angle with microwaves is feasible. The second application is electrical impedance tomography, where the conductivity of an object is estimated from surface measurements. Electrical impedance tomography has applications in, for example, medical imaging, geological surveillance, and wood evaluation. Different configurations and noise models are evaluated with sensitivity analysis for a two-dimensional electrical impedance tomography problem. The relation between the accuracy and resolution is also analysed using the Fisher information. To conclude, sensitivity analysis is employed in this thesis, as a method to enhance material parameter estimation. The sensitivity analysis methods are general and applicable also on other parameter estimation problems. / Estimering av parametrar är att finna deras värde utifrån mätningar och modellering. Parametrar beskriver egenskaper hos system och till exempel material kan definieras med mekaniska, elektriska och kemiska parametrar. Fisherinformation är ett informationsmått som ger information om hur ändringar i en parameter påverkar estimeringen. Fisherinformationen ges av en fysikalisk modell av problemet och en statistisk modell av mätbruset. Cramér-Rao-gränsen är inversen av Fisherinformationen och ger den bästa möjliga variansen för alla väntevärdesriktiga estimatorer.Den här avhandlingen behandlar aspekter av känslighetsanalys i två tillämpade estimeringsproblem för materialparametrar. Känslighetsanalys med Fisherinformation och Cramér-Rao-gränsen används som ett redskap för utvärdering av möjligheten att mäta och för jämförelser av mätuppställningar, samt som ett kvantitativt mått på avvägningen mellan noggrannhet och upplösning för inversa bilder. Den första tillämpningen är estimering av fibervinkeln hos träd och stockar. Fibervinkeln är vinkeln mellan växtriktningen och riktningen hos träfibern och en stor fibervinkel är relaterad till problem med formstabilitet i färdiga brädor. Mikrovågsmätningar av fibervinkeln presenteras som en snabb och robust mätteknik. I avhandlingen beskrivs två- och tredimensionella elektromagnetiska modeller som utnyttjar anisotropin hos trä. Eftersom matematisk modellering minskar komplexiteten och beräkningstiden är det en viktig del i estimeringen. Enligt känslighetsanalys med Cramér-Rao-gränsen är estimering av fibervinkeln hos trä möjlig. Den andra tillämpningen är elektrisk impedanstomografi, där ledningsförmågan hos objekt bestäms genom mätningar på ytan. Elektrisk impedanstomografi har tillämpningar inom till exempel medicinska bilder, geologisk övervakning och trämätningar. Olika mätkonfigurationer och brusmodeller utvärderas med känslighetsanalys för ett tvådimensionellt exempel på elektrisk impedanstomografi. Relationen mellan noggrannhet och upplösning analyseras med Fisher information. För att sammanfatta beskrivs känslighetsanalys som en metod för att förbättra estimeringen av materialparametrar. Metoderna för känslighetsanalys är generella och kan tillämpas också på andra estimeringsproblem för parametrar.

This dissertation studies several aspects of the interface between information theory and signal processing. Several new and existing results in information theory are researched from the perspective of signal processing. Similarly, some fundamental results in signal processing and statistics are studied from the information theoretic viewpoint. The first part of this dissertation focuses on illustrating the equivalence between Stein's identity and De Bruijn's identity, and providing two extensions of De Bruijn's identity. First, it is shown that Stein's identity is equivalent to De Bruijn's identity in additive noise channels with specific conditions. Second, for arbitrary but fixed input and noise distributions, and an additive noise channel model, the first derivative of the differential entropy is expressed as a function of the posterior mean, and the second derivative of the differential entropy is expressed in terms of a function of Fisher information. Several applications over a number of fields, such as statistical estimation theory, signal processing and information theory, are presented to support the usefulness of the results developed in Section 2. The second part of this dissertation focuses on three contributions. First, a connection between the result, proposed by Stoica and Babu, and the recent information theoretic results, the worst additive noise lemma and the isoperimetric inequality for entropies, is illustrated. Second, information theoretic and estimation theoretic justifications for the fact that the Gaussian assumption leads to the largest Cramer-Rao lower bound (CRLB) is presented. Third, a slight extension of this result to the more general framework of correlated observations is shown. The third part of this dissertation concentrates on deriving an alternative proof for an extremal entropy inequality (EEI), originally proposed by Liu and Viswanath. Compared with the proofs, presented by Liu and Viswanath, the proposed alternative proof is simpler, more direct, and more information-theoretic. An additional application for the extremal inequality is also provided. Moreover, this section illustrates not only the usefulness of the EEI but also a novel method to approach applications such as the capacity of the vector Gaussian broadcast channel, the lower bound of the achievable rate for distributed source coding with a single quadratic distortion constraint, and the secrecy capacity of the Gaussian wire-tap channel. Finally, a unifying variational and novel approach for proving fundamental information theoretic inequalities is proposed. Fundamental information theory results such as the maximization of differential entropy, minimization of Fisher information (Cramer-Rao inequality), worst additive noise lemma, entropy power inequality (EPI), and EEI are interpreted as functional problems and proved within the framework of calculus of variations. Several extensions and applications of the proposed results are briefly mentioned.