An autonomous, real-time orbit determination system was developed within thiswork for the next iteration of the University of W¨urzburg’s CubeSat programme.The algorithm only made use of magnetometer and sun sensors, which already wereimplemented on UWE-3, the third satellite in the programme. Previous developedsystems used the same approach, however the unique aspect in this work is thatthe algorithm was implemented using equinoctial elements.A Runge-Kutta-4 integrator propagated the orbit position using the orbit dynamicsunder the consideration of J2-perturbations. Afterwards, an Extended KalmanFilter corrected the position through processing the two measurements.The algorithm was then tested under multiple conditions. At first, a two weekstability test was conducted using simulated data, followed by a test with recordedsatellite data. These have shown a mean error of 13.2 km and 12.6 km respectively.Lastly, the algorithm was translated in to C and evaluated on a micro-controller.

The objective of this research is to study various methods for censoring state estimate updates generated from radar measurements. The generated 2-D radar data are sent to a fusion center using the J-Divergence metric as the means to assess the quality of the data. Three different distributed sensor network architectures are considered which include different levels of feedback. The Extended Kalman Filter (EKF) and the Gaussian Particle Filter (GPF) were used in order to test the censoring methods in scenarios which vary in their degrees of non-linearity. A derivation for the direct calculation of the J-Divergence using a particle filter is provided. Results show that state estimate updates can be censored using the J-Divergence as a metric controlled via feedback, with higher J-Divergence thresholds leading to a larger covariance at the fusion center.

The ensemble Kalman filter (EnKF) is a reliable data assimilation tool for high-dimensional meteorological problems. On the other hand, the EnKF can be interpreted as a particle filter, and particle filters (PF) collapse in high-dimensional problems. We explain that these seemingly contradictory statements offer insights about how PF function in certain high-dimensional problems, and in particular support recent efforts in meteorology to 'localize' particle filters, i.e. to restrict the influence of an observation to its neighbourhood.

Structural health monitoring (SHM) is a quickly advancing field of study in civil engineering and recent advances in the field are in stark contrast to where the field started. For example modern technology of wireless sensing systems allowed for easier monitoring of structures, but the challenge of limiting the number of instrumented locations has not been overcome with traditional methods. The potential of alternative methods has only been realized in recent years with the increase of model based approaches. In particular, the use of limited measurements to estimate structural response at all locations is appealing. To accomplish this goal, this work approaches SHM by using a numerical model combined with a linear recursive state estimation algorithm, known as the Kalman Filter, to update the model-based prediction with a limited number of real time measurements taken on the structure. A thorough overview of the contents is given here. The first section introduces the topic of SHM and the goal of SHM. Then the challenges and limitation that face SHM are discussed along with the recent advances that can be used to overcome them. In Section 2, the proposed framework, a Kalman filter approach, is established. First, a finite element model is formulated for plate structures using the Mindlin-Reissner plate theory and then this finite element code is verified by a comparison with a commercial FEA software. Then the state space model of the system is defined for use with the Augmented Kalman Filter (AKF); the AKF approach overcomes the intrinsic challenge of unknown excitations for civil structures. The AKF is then formulated and discussed. For Section 3, using the AKF in numerical simulations are conducted for 5 different cases. The first three cases study the advantages of multi-metric measurements, i.e. strain and acceleration measurements combined, versus single metric measurement, i.e. strain measurement only or acceleration measurement only. Following that, the next two cases explore the question of whether multi-metric measurements will always provide the best results. Based on the conclusions from the previous section, Section 4 investigates the application of a genetic algorithm, a search algorithm based of Darwinian principles, to find the optimal sensor placement to use as the input to the AKF. Here the developed search algorithm is used in two cases, the first is to find the optimal placement for the strain measurement only case. Next, the improvements in accuracy that are gained by placing taking more measurements is investigated to determine if the gain in accuracy per added measurement decreases for large numbers of measurements. Section 5 contains the final conclusions about the use of the AKF for SHM of plate structures then the potential opportunities of future work regarding plate structures are discussed.

In this paper a resilient sensor network is built in order to lessen the effects of a small portion of corrupted sensors when an aggregated result such as the average needs to be obtained. By examining the variance in sensor readings, a change in the pattern can be spotted and minimized in order to maintain a stable aggregated reading. Offset in sensors readings are also analyzed and compensated to help reduce a bias change in average. These two analytical techniques are later combined in Kalman filter to produce a smooth and resilient average given by the readings of individual sensors. In addition, principal components analysis is used to detect variations in the sensor network. Experiments are held using real sensors called MICAz, which are use to gather light measurements in a small area and display the light average generated in that area.

O eletrocardiograma transMITe informações com relação à passagem do pulso elétrico pelo coração e, conseqüentemente, do funcionamento deste. Desde o início da sua utilização, possibilitada pelo trabalho de Willem Einthoven criando a primeira máquina capaz de medir o pulso elétrico de forma não invasiva e com sensibilidade forte o bastante para ser capaz de produzir um gráfico proveitoso, o eletrocardiograma é muito utilizado para avaliação clínica de pacientes. Entretanto a evolução das máquinas que o descrevem não foi muito além do que o elaborado por Einthoven no início do século 20. As máquinas capazes de captar o eletrocardiograma se tornaram menores (até portáteis para algumas aplicações), gráficos passaram a ser disponibilizados em telas de vídeo (ao invés das fitas de papel) e, como maior evolução, as máquinas que observam o eletrocardiograma passaram a conseguir captar a ocorrência de um ciclo cardíaco com alta confiabilidade e, atualmente, passaram a medir também o parâmetro ST com precisão deliMITada (necessitando ajuda do operador para ajuste em alguns casos). É baseado nestes fatos que esta dissertação procura estudar algoritmos matemáticos, de forma mais focada nos modelos do impulso elétrico durante os ciclos cardíacos, e avaliar suas capacidades de interpretar parâmetros do ciclo de ECG de forma precisa e rápida para que o médico tenha prontamente os dados necessários para realizar a avaliação clínica do paciente. Em primeira análise foram estudados os algoritmos para detecção do pulso de eletrocardiograma (detecção da onda R), em seguida feito o janelamento da curva de ECG a fim de separar os ciclos cardíacos. A partir deste ponto foram analisados os modelos matemáticos gerados por equações polinomiais, Transformada de Fourier e Transformada wavelet. E, com o intuito de filtrar ruídos e gerar derivações não medidas, foi implementado um filtro de kalman em um modelo vetorial do eletrocardiograma. Para avaliar os resultados obtidos foram utilizados requisitos de desempenho declarados pelo FDA norte americano e pela norma européia IEC60601-2-51. Essas análises foram feitas através da utilização dos algoritmos gerados nas curvas provindas do banco de dados do PhisioNet. O método polinomial não foi considerado interessante na medida em que não possibilita gerar uma equação para um ciclo cardíaco, mas sim várias equações (uma para cada ponto do ciclo). Os demais métodos apresentaram melhor eficiência na medida em que foram capazes de gerar parâmetros com significado físico e possibilitando melhor caracterização de pontos importantes da curva do eletrocardiograma. / The electrocardiogram gives information related to the passage of an electric pulse through the heart and, therefore, to his state function. Since the beginning of electrocardiogram utilization, thanks to the work of Willem Einthoven building the first machine capable of measuring the electric pulse non-invasively and with sensitivity enough to be able to provide a profitable graph, it is widely used for clinical evaluation of patients. However the evolution of the machines that describes the electrocardiogram hadn´t much more advances since the elaborated by Einthoven in the beginning of the 20th century. They become smaller (even portable for some applications), the graphs are now displayed in video screens (instead of the paper strip) and, taking place as the biggest evolutions, machines that observes the electrocardiogram became able to recognize a cardiac cycle with high reliability and, more recently, became able to measure the ST parameter with liMITed precision (it needs the help of the operator to set specific measuring points in some cases). It is based in these facts that this dissertation looks for analyzing mathematic algorithms, more specifically the mathematic models of the electric impulse during the cardiac cycles, and evaluate their capacities to expound ECG parameters in a fast and reliable way in order to the physician receive promptly the data needed for his clinical evaluation of the patient. For the first step were analyzed some algorithms for electrocardiogram pulse detection (detection of R wave), in the following step were done the windowing of the ECG wave in order to separate the cardiac cycles. In this step were analyzed the mathematic models generated by polynomial equations, Fourier Transform and Wavelet Transform. And, in order to filter noises and generate leads not measure, it was implemented a kalman´s filter at a vector model. To evaluate the obtained results were used the requirements of performance given by north-american FDA and by the European rule IEC60601-2-51. These evaluations were done by executing the generated algorithms in the waves supplied by the databank PhisioNet. The polynomial method weren´t considered interesting because it weren´t able to generate an equation for the cardiac cycle, but many equations (one for each point of the cycle). The other methods showed a better efficiency since they were capable of generate parameters with physical meaning and being able to do a better characterization of the important points of the electrocardiogram wave.

Inertial navigation is a relative navigation technique commonly used by autonomous vehicles to determine their linear velocity, position and orientation in three-dimensional space. The basic premise of inertial navigation is that measurements of acceleration and angular velocity from an inertial measurement unit (IMU) are integrated over time to produce estimates of linear velocity, position and orientation. However, this process is a particularly involved one. The raw inertial data must first be properly analyzed and modeled in order to ensure that any inertial navigation system (INS) that uses the inertial data will produce accurate results. This thesis describes the process of analyzing and modeling raw IMU data, as well as how to use the results of that analysis to design an INS. Two separate INS units are designed using two different micro-electro-mechanical system (MEMS) IMUs. To test the effectiveness of each INS, each IMU is rigidly mounted to an unmanned ground vehicle (UGV) and the vehicle is driven through a known test course. The linear velocity, position and orientation estimates produced by each INS are then compared to the true linear velocity, position and orientation of the UGV over time. Final results from these experiments include quantifications of how well each INS was able to estimate the true linear velocity, position and orientation of the UGV in several different navigation scenarios as well as a direct comparison of the performances of the two separate INS units.

"This work addresses the problem of generating a 3D mesh grid model of a tree by a climbing robot for tree inspection. In order to generate a consistent model of the tree while climbing, the robot needs to be able to track its location while generating the model. Hence we explored this problem as a subset of Simultaneous Localization and Mapping problem. The monocular camera based Visual Simultaneous Localization and Mapping(VSLAM) algorithm was adopted to map the features on the tree. Multi-scale grid based FAST feature detector combined with Lucas Kande Optical flow was used to extract features from the tree. Inverse depth representation of feature was selected to seamlessly handle newly initialized features. The camera and the feature states along with their co-variances are managed in an Extended Kalman filter. In our VSLAM implementation we have attempted to track a large number of features. From the sparse spatial distribution of features we get using Extended Kalman filter we attempt to generate a 3D mesh grid model with the help of an unordered triangle fitting algorithm. We explored the implementation in C++ using Eigen, OpenCV and Point Cloud Library. A multi-threaded software design of the VSLAM algorithm was implemented. The algorithm was evaluated with image sets from trees susceptible to Asian Long Horn Beetle. "

This thesis presents a 6 degree of freedom (DOF) position and orientation tracking solution suitable for pedestrian motion tracking based on 6DOF low cost MEMS inertial measurement units. This thesis was conducted as an extension of the ongoing efforts of the Precision Personnel Location (PPL) project at WPI. Prior to this work most of the PPL research focus has been on Radio Frequency (RF) location estimation. The newly developed inertial based system supports data fusion with the aforementioned RF system in a system currently under development. This work introduces a methodology for the implementation of a position estimation system based upon a Kalman filter structure, constructed from industry standard inertial sensor specifications and analytic noise models. This methodology is important because it allows for both rapid filter construction derived solely from specified values and flexible system definitions. In the course of the project, three different sensors were accommodated using the automatic design tools that were constructed. This thesis will present the mathematical basis of the new inertial tracking system followed by the stages of filter design and implementation, and finally the results of several trials with actual inertial data captures, using both public reference data and inertial captures from a foot mounted sensor that was developed as part of this work.

Um componente fundamental no sistema de um robô móvel consiste na habilidade de localizar-se acuradamente, o que envolve estimar sua postura em relação a uma representação global do espaço. A especificação geral de uma abordagem de localização baseada em dados sensoriais possui uma estimativa inicial da postura do robô e usa os dados coletados pelos sensores, em conjunto com um mapa do ambiente, para produzir uma estimativa mais precisa da postura, que oferece um valor de maior confiança em relação à postura real do robô. Uma dificuldade é que os dados sensoriais são corrompidos por erros de medidas derivados de diversas fontes, como ruídos, quantização, dispositivos de digitalização, deslizamentos do robô, entre outras. Sensores distintos medem diferentes propriedades físicas, corrompidas por diversos erros de medida. O uso de dados oriundos de vários sensores fornece informação redundante e complementar, que pode ser processada para derivar uma estimativa combinada com o objetivo de aumentar a confiança na estimativa final da postura. Nesta dissertação é proposto ELViS, um sistema que estima a localização de um robô móvel equipado com odômetros, uma câmera de vídeo e um semi-anel frontal de 8 sonares, o qual opera, com sucesso, em um ambiente interno, estruturado e estático. Assume-se que o robô navega sobre uma superfície plana e que diversos segmentos de retas possam ser identificados nas imagens do ambiente. Para aumentar a seletividade dos marcos visuais e diminuir a complexidade computacional no processamento e correspondência dos dados com os modelos, elementos do ambiente são representados por modelos minimalistas, possibilitando o uso do ELViS em um grande número de aplicações onde o custo ou tempo de execução sejam fatores limitantes. ELViS foi implementado e testado utilizando dois estimadores baseados em Filtro de Kalman. Os resultados, obtidos com robôs reais e em simulações, indicam direções bastante promissoras. / A key component of a mobile robot system is the ability to localize itself accurately, which involves estimating its pose with respect to some global representation of space. The general specification of a sensor-based localization approach starts with an initial estimate of the robot\'s pose and uses sensor data in conjunction with a map to produce a refined pose estimate that has an increased confidence about the true pose of the robot. One of the main difficulties is that sensor data is corrupted by measurement errors. These errors can arise from noise, quantization, digitalization artifacts, wheel slippage, and other such sources. Different sensors measure different physical properties, which are corrupted by different sources of measurement errors. The use of data from multiple sensors provides redundant and complementary information that can be processed to obtain a combined estimate aiming at an increase in the confidence of the final pose estimate. In this work we propose ELViS, a system that estimates the localization of a mobile robot equipped with odometers, a video camera and a frontal semi-ring of 8 sonar sensors, and that operates successfully in stationary and structured indoor environments. It is assumed that the robot navigates on flat surfaces and that straight lines can be identified in the environment image acquired by the camera. To increase selectivity of the landmarks and reduce computational complexity in data processing and matching to the map, environment features are represented using minimalist models in the map. This allows the use of ELViS in a large number of applications where tight budget or execution time constraints exist. ELViS has been implemented and tested using two estimators based on the Kalman Filter. The results, obtained with the real robots and in series of simulation runs, indicate promising directions.