The experimental determination of dynamic characteristics of elastic structures, in particularof space flight related structures typically is performed by experimental modal analysis(EMA) or output-only modal analysis (OMA). This document is focused on the OMA methodsand state-space modelling, the motivation for this approach is the possibility to monitorthe real loading of a structure in order to provide a loading history which may be used foran assessment of safe remaining life once the dynamic characteristics has been determined. Previous work has demonstrated that Extened Kalman Filter is not sufficient in thecase when the forces are unkown and the only resource available are the responses of thestructure. In this research a new method called Unscented Kalman Filter is investigated andimplemented, proving its capability to obtain a better approximation of the elastic structurebehaviour and a correction of the modal parameters.

This work was motivated by the idea of developing a more encompassing collision avoidance system that supported vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. Current systems are mostly based on line of sight sensors that are used to prevent a collision, but these systems would prevent even more accidents if they could detect possible collisions before both vehicles were in line of sight. For this research we concentrated mostly on the aspect of improving the prediction of a vehicle's future trajectory, particularly on non-straight paths. Having an accurate prediction of where the vehicle is heading is crucial for the system to reliably determine possible path intersections of more than one vehicle at the same time. We first evaluated the benefits of merging Global Positioning System (GPS) data with the Geographical Information System (GIS) data to correct improbable predicted positions. We then created a new algorithm called the Dead Reckoning with Dynamic Errors (DRWDE) sensor fusion, which can predict future positions at the rate of its fastest sensor, while improving the handling of accumulated error while some of the sensors are offline for a given period of time. The last part of out research consisted in the evaluation of the use of smartphones' built-in sensors to predict a vehicle's trajectory, as a possible intermediate solution for a vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications, until all vehicles have all the necessary sensors and communication infrastructure to fully populate this new system. For the first part of our research, the actual experimental results validated our proposed system, which reduced the position prediction errors during curves to around half of what it would be without the use of GIS data for prediction corrections. The next improvement we worked on was the ability to handle change in noise, depending on unavailable sensor measurements, permitting a flexibility to use any type of sensor and still have the system run at the fastest frequency available. Compared to a more common KF implementation that run at the rate of its slowest sensor (1Hz in our setup), our experimental results showed that our DRWDE (running at 10Hz) yielded more accurate predictions (25-50% improvement) during abrupt changes in the heading of the vehicle. The last part of our research showed that, comparing to results obtained with the vehicle-mounted sensors, some smartphones yield similar prediction errors and can be used to predict a future position.

Approved for public release; distribution is unlimited / "Human body motion tracking using inertial sensors requires an attitude estimation filter capable of tracking in all orientations. One way to represent orientation is to use Euler angles, but they have singularities and therefore are not suitable for human body tracking applications. Quaternions can be used to represent orientation without incurring singularities. A quaternion-based Kalman filter has been designed for this purpose and implemented in this thesis. Also, a new suboptimal algorithm to compute the quaternions based on magnetometer and accelerometer data is implemented. This new algorithm called "Factored Quaternion Algorithm" is computationally simpler than previous methods and provides a decoupling property from magnetometer and accelerometer data." p. i. / Lieutenant Junior Grade, Mexican Navy

Doctor of Philosophy / Department of Economics / Steven P. Cassou / This dissertation consists of three essays relating to asymmetric preferences in optimal monetary policy models. Optimal monetary policy models are theoretical optimal control problems that seek to identify how the monetary authority makes decisions and ultimately formulate decision rules for monetary policy actions. These models are important to policy makers because they help to define expectations of policy responses by the central bank. By identifying how researchers perceive the central bank’s actions over time, the monetary authority can identify how to manage those expectations better and formulate effective policy measures. In chapter 1, using a model of an optimizing monetary authority which has preferences that weigh inflation and unemployment, Ruge-Murcia (2003a; 2004) finds empirical evidence that the monetary authority has asymmetric preferences for unemployment. We extend this model to weigh inflation and output and show that the empirical evidence using these series also supports an asymmetric preference hypothesis, only in our case, preferences are asymmetricforoutput. Wealsofindevidencethatthemonetaryauthoritytargetspotential output rather than some higher output level as would be the case in an extended Barro and Gordon (1983) model. Chapter 2 extends the asymmetric monetary policy problem of Surico (2007) by relaxing the assumption that inflation and interest rate targets are constant using a time varying parameter approach. By estimating a system of equations using iterative maximum likeli- hood, all of the monetary planner’s structural parameters are identified. Evidence indicates that the inflation and interest rate targets are not constant over time for all models esti- mated. Results also indicate that the Federal Reserve does exhibit asymmetric preferences toward inflationary and output gap movements for the full data sample. The results are robust when accounting for changing monetary policy targeting behavior in an extended model. The asymmetry for both inflation and output gaps disappears over the post-Volcker subsample, as in Surico (2007). In chapter 3, Walsh (2003b)’s speed limit objective function is generalized to allow for asymmetry of policy response. A structural model is estimated using unobserved compo- nents to account for core inflation and measure the output gap as in Harvey, Trimbur and Van Dijk (2007) and Harvey (2011). Full sample estimates provide evidence for asymmetry in changes in inflation over time, but reject asymmetry for the traditional speed limit for the output gap. Post-Volcker subsample estimates see asymmetry disappear as in a more traditional asymmetric preferences model like Surico (2007).

A navigation filter simulation and analysis environment was developed through the integration of DRAGON, a high fidelity real-time PNT sensor measurement source, and Scorpion, a modular navigation filter implementation framework. The envi- ronment allows navigation filters to be prototyped and tested in varying complex scenarios with a configurable set of navigation sensors including GNSS and IMU. An analysis of an EKF using the environment showed the utility and functionality of the system.

A navigation filter simulation and analysis environment was developed through the integration of DRAGON, a high fidelity real-time PNT sensor measurement source, and Scorpion, a modular navigation filter implementation framework. The envi- ronment allows navigation filters to be prototyped and tested in varying complex scenarios with a configurable set of navigation sensors including GNSS and IMU. An analysis of an EKF using the environment showed the utility and functionality of the system.

Orientador: Vicente Lopes Junior / Co-orientador: Gustavo Luiz Chagas Manhães de Abreu / Banca: Michael John Brennan / Banca: Paulo José Paupitz Gonçalves / Resumo: Neste trabalho são apresentados dois métodos de identificação de modelos em espaço de estados. O primeiro, o Algoritmo de Realização de Autosistemas, (ERA), identifica matrizes de estado através da resposta do sistema ao impulso. Já o segundo, o método ERA/OKID, também estima as matrizes de estado do sistema, com uma vantagem que não se limita a resposta do sistema ao impulso, mas qualquer sinal pode ser usado como sinal de entrada. Os dois métodos foram aplicados na identificação experimental de uma viga de alumínio engastada. O sinal de entrada foi aplicado na viga através de um atuador PZT (Lead-Zirconate-Titanate) e a resposta foi medida através de um sensor PVDF (Polyvinilidene-Fluoride). Com as matrizes de estado identificadas, projetou-se um controlador para a realimentação de estados. O controle Regulador Linear Quadrático (LQR), foi utilizado pela simplicidade da formulação e fácil implementação. Para realimentar os estados não mensurados, foi projetado um observador de estados. O controle aplicado à estrutura foi capaz de atenuar as vibrações quando a mesma foi submetida a diferentes tipos de perturbações externas / Abstract: This work presents two methods of system identification of models in state space. The first method, uses Eigensystem Realization Algorithm, (ERA), for identifying the state space matrices via impulse response of the system. The second method, ERA/OKID, also identifies state space matrices, however, in this method, the input data are not limited to the impulsive response, and any signal can be used as input signal. It can be a significant advantage for practical situations. Both methods were applied for experimental identification of a cantilever aluminium beam. The input excitation in beam used white noise through a (Lead-Zirconate-Titanate) PZT actuator and the beam response was measured using a PVDF (Polyvinilidene-Fluoride) sensor. The controller was designed for state feedback using the state space matrices obtained previously. The Linear Quadratic Regulator, (LQR), was used for simplicity of design and easy implementation. A state observer was also used to feedback the unmeasured states. The controller was effective to minimize the vibrations of the structure when it was subjected to an external disturbance / Mestre

Autonomous aerial, terrestrial, and marine vehicles provide a platform for several applications including cargo transport, information gathering, surveillance, reconnaissance, and search-and-rescue. To enable such applications, two main technical problems are commonly addressed.On the one hand, the motion-planning problem addresses optimal motion to a destination: an application example is the delivery of a package in the shortest time with least fuel. Solutions to this problem often assume that all relevant information about the environment is available, possibly with some uncertainty. On the other hand, the information gathering problem addresses the maximization of some metric of information about the environment: application examples include such as surveillance and environmental monitoring. Solutions to the motion-planning problem in vehicular autonomy assume that information about the environment is available from three sources: (1) the vehicle’s own onboard sensors, (2) stationary sensor installations (e.g. ground radar stations), and (3) other information gathering vehicles, i.e., mobile sensors, especially with the recent emphasis on collaborative teams of autonomous vehicles with heterogeneous capabilities. Each source typically processes the raw sensor data via estimation algorithms. These estimates are then available to a decision making system such as a motion- planning algorithm. The motion-planner may use some or all of the estimates provided. There is an underlying assumption of “separation� between the motion-planning algorithm and the information about environment. This separation is common in linear feedback control systems, where estimation algorithms are designed independent of control laws, and control laws are designed with the assumption that the estimated state is the true state. In the case of motion-planning, there is no reason to believe that such a separation between the motion-planning algorithm and the sources of estimated environment information will lead to optimal motion plans, even if the motion planner and the estimators are themselves optimal. The goal of this dissertation is to investigate whether the removal of this separation, via interactive motion-planning and sensing, can significantly improve the optimality of motion- planning. The major contribution of this work is interactive planning and sensing. We consider the problem of planning the path of a vehicle, which we refer to as the actor, to traverse a threat field with minimum threat exposure. The threat field is an unknown, time- variant, and strictly positive scalar field defined on a compact 2D spatial domain – the actor’s workspace. The threat field is estimated by a network of mobile sensors that can measure the threat field pointwise. All measurements are noisy. The objective is to determine a path for the actor to reach a desired goal with minimum risk, which is a measure sensitive not only to the threat exposure itself, but also to the uncertainty therein. A novelty of this problem setup is that the actor can communicate with the sensor network and request that the sensors position themselves in a procedure we call sensor reconfiguration such that the actor’s risk is minimized. This work continues with a foundation in motion planning in time-varying fields where waiting is a control input. Waiting is examined in the context of finding an optimal path with considerations for the cost of exposure to a threat field, the cost of movement, and the cost of waiting. For example, an application where waiting may be beneficial in motion-planning is the delivery of a package where adverse weather may pose a risk to the safety of a UAV and its cargo. In such scenarios, an optimal plan may include “waiting until the storm passes.� Results on computational efficiency and optimality of considering waiting in path- planning algorithms are presented. In addition, the relationship of waiting in a time- varying field represented with varying levels of resolution, or multiresolution is studied. Interactive planning and sensing is further developed for the case of time-varying environments. This proposed extension allows for the evaluation of different mission windows, finite sensor network reconfiguration durations, finite planning durations, and varying number of available sensors. Finally, the proposed method considers the effect of waiting in the path planner under the interactive planning and sensing for time-varying fields framework. Future work considers various extensions of the proposed interactive planning and sensing framework including: generalizing the environment using Gaussian processes, sensor reconfiguration costs, multiresolution implementations, nonlinear parameters, decentralized sensor networks and an application to aerial payload delivery by parafoil.

Las metodologías para la determinación e identificación de fallas en procesos industriales viene siendo desarrollada e investigada desde hace 30 años, en los cuales se han elaborado una gran variedad de metodologías de detección y de aplicaciones a sistemas reales. Debido al aumento de la complejidad y cantidad de los procesos que necesitan ser controlados, surgen teorías para la detección e identificación de fallas como solución a problemas de repercusión no solo económica, sino también ecológica, productiva y de seguridad. En la presente tesis se ha desarrollado un método de detección e identificación basado en una innovación proveniente del filtro de Kalman, la cual provee condiciones suficientes y necesarias para la detección de fallas aditivas bajo influencia de ruido gaussiano blanco. Esta metodología de detección se aplica a un motor de corriente contínua de excitación independiente, cuya función de transferencia tipo SISO ha sido obtenida experimentalmente. Posteriormente un análisis estadístico de la innovación obtenida del filtro de Kalman, ha permitido diagnosticar la presencia e instante de la falla aditiva generada en el sensor del sistema. Lo anterior ha generado un sistema capaz de detectar fallas aditivas idealizadas como modelos tipo escalones y rampas en un sistema lineal e invariante en el tiempo. El sistema desarrollado, permite la correcta detección e identificación de las fallas aditivas presentes en el sensor del modelo del motor de corriente continua, basándose en el análisis estadístico del parámetro innovación proveniente del Filtro de Kalman. / Tesis

This thesis deals with automatic localization and tracking of surgical tools such as needles in Magnetic Resonance Imaging(MRI). The accurate and precise localization of needles is very important for medical interventions such as biopsy, brachytherapy, anaesthesia and many other needle based percutaneous interventions. Needle tracking has to be really precise, because the target may reside adjacent to organs which are sensitive to injury. More over during the needle insertion, Magnetic Resonance Imaging(MRI) scan plane must be aligned such that needle is in the field of view (FOV) for surgeon. Many approaches were proposed for needle tracking and automatic MRI scan plane control over last decade that use external markers, but they are not able to account for possible needle bending. Significant amount of work has already been done by using the image based approaches for needle tracking in Image Guided Therapy (IGT) but the existing approaches for surgical robots under MRI guidance are purely based on imaging information; they are missing the important fact that, a lot of important information (for example, depth of insertion, entry point and angle of insertion) is available from the kinematic model of the robot. The existing approaches are also not considering the fact that the needle insertion results in a time sequence of images. So the information about needle positions from the images seen so far can be used to make an approximate estimate about the needle position in the subsequent images. During the course of this thesis we have investigated an image based approach for needle tracking in real-time MR images that leverages additional information available from robot's kinematics model, supplementing the acquired images. The proposed approach uses Standard Hough Transform(SHT) for needle detection in 2D MR image and uses Kalman Filter for tracking the needle over the sequence of images. We have demonstrated experimental validation of the method on Real MRI data using gel phantom and artificially created test images. The results proved that the proposed method can track the needle tip position with root mean squared error of 1.5 mm for straight needle and 2.5mm for curved needle.