<p>Automotive positioning systems relying exclusively on the input from a GPS receiver, which is a line of sight sensor, tend to be sensitive to situations with limited sky visibility. Such situations include: urban environments with tall buildings; inside parking structures; underneath trees; in tunnels and under bridges. In these situations, the system has to rely on integration of relative motion sensors to estimate vehicle position. However, these sensor measurements are generally affected by errors such as offsets and scale factors, that will cause the resulting position accuracy to deteriorate rapidly once GPS input is lost. </p><p>The approach in this thesis is to use a GPS receiver in combination with low cost sensor equipment to produce a robust positioning module. The module should be capable of handling situations where GPS input is corrupted or unavailable. The working principle is to calibrate the relative motion sensors when GPS is available to improve the accuracy during GPS intermission. To fuse the GPS information with the sensor outputs, different models have been proposed and evaluated on real data sets. These models tend to be nonlinear, and have therefore been processed in an Extended Kalman Filter structure. </p><p>Experiments show that the proposed solutions can compensate for most of the errors associated with the relative motion sensors, and that the resulting positioning accuracy is improved accordingly.</p>

<p>A face tracker is exactly what it sounds like. It tracks a face in a video sequence. Depending on the complexity of the tracker, it could track the face as a rigid object or as a complete deformable face model with face expressions.</p><p>This report is based on the work of a real time feature based face tracker. Feature based means that you track certain features in the face, like points with special characteristics. It might be a mouth or eye corner, but theoretically it could be any point. For this tracker, the latter is of interest. Its task is to extract global parameters, i.e. rotation and translation, as well as dynamic facial parameters (expressions) for each frame. It tracks feature points using motion between frames and a textured face model (Candide). It then uses an extended Kalman filter to estimate the parameters from the tracked feature points.</p>

Automotive positioning systems relying exclusively on the input from a GPS receiver, which is a line of sight sensor, tend to be sensitive to situations with limited sky visibility. Such situations include: urban environments with tall buildings; inside parking structures; underneath trees; in tunnels and under bridges. In these situations, the system has to rely on integration of relative motion sensors to estimate vehicle position. However, these sensor measurements are generally affected by errors such as offsets and scale factors, that will cause the resulting position accuracy to deteriorate rapidly once GPS input is lost. The approach in this thesis is to use a GPS receiver in combination with low cost sensor equipment to produce a robust positioning module. The module should be capable of handling situations where GPS input is corrupted or unavailable. The working principle is to calibrate the relative motion sensors when GPS is available to improve the accuracy during GPS intermission. To fuse the GPS information with the sensor outputs, different models have been proposed and evaluated on real data sets. These models tend to be nonlinear, and have therefore been processed in an Extended Kalman Filter structure. Experiments show that the proposed solutions can compensate for most of the errors associated with the relative motion sensors, and that the resulting positioning accuracy is improved accordingly.

There is currently a wide variety of six degree-of-freedom (6-DOF) motion capture technologies available. However, these systems tend to be very expensive and thus cost prohibitive. A software system was developed to provide 6-DOF motion capture using the Nintendo Wii remote’s (wiimote) sensors, an infrared beacon, and a novel hierarchical linear-quaternion Kalman filter. The software is made freely available, and the hardware costs less than one hundred dollars. Using this motion capture software, a robotic control system was developed to teleoperate a 6-DOF robotic manipulator via the operator’s natural hand movements. The teleoperation system requires calibration of the wiimote’s infrared cameras to obtain an estimate of the wiimote’s 6-DOF pose. However, since the raw images from the wiimote’s infrared camera are not available, a novel camera-calibration method was developed to obtain the camera’s intrinsic parameters, which are used to obtain a low-accuracy estimate of the 6-DOF pose. By fusing the low-accuracy estimate of 6-DOF pose with accelerometer and gyroscope measurements, an accurate estimation of 6-DOF pose is obtained for teleoperation. Preliminary testing suggests that the motion capture system has an accuracy of less than a millimetre in position and less than one degree in attitude. Furthermore, whole-system tests demonstrate that the teleoperation system is capable of controlling the end effector of a robotic manipulator to match the pose of the wiimote. Since this system can provide 6-DOF motion capture at a fraction of the cost of traditional methods, it has wide applicability in the field of robotics and as a 6-DOF human input device to control 3D virtual computer environments.

There is currently a wide variety of six degree-of-freedom (6-DOF) motion capture technologies available. However, these systems tend to be very expensive and thus cost prohibitive. A software system was developed to provide 6-DOF motion capture using the Nintendo Wii remote’s (wiimote) sensors, an infrared beacon, and a novel hierarchical linear-quaternion Kalman filter. The software is made freely available, and the hardware costs less than one hundred dollars. Using this motion capture software, a robotic control system was developed to teleoperate a 6-DOF robotic manipulator via the operator’s natural hand movements. The teleoperation system requires calibration of the wiimote’s infrared cameras to obtain an estimate of the wiimote’s 6-DOF pose. However, since the raw images from the wiimote’s infrared camera are not available, a novel camera-calibration method was developed to obtain the camera’s intrinsic parameters, which are used to obtain a low-accuracy estimate of the 6-DOF pose. By fusing the low-accuracy estimate of 6-DOF pose with accelerometer and gyroscope measurements, an accurate estimation of 6-DOF pose is obtained for teleoperation. Preliminary testing suggests that the motion capture system has an accuracy of less than a millimetre in position and less than one degree in attitude. Furthermore, whole-system tests demonstrate that the teleoperation system is capable of controlling the end effector of a robotic manipulator to match the pose of the wiimote. Since this system can provide 6-DOF motion capture at a fraction of the cost of traditional methods, it has wide applicability in the field of robotics and as a 6-DOF human input device to control 3D virtual computer environments.

<p dir="ltr">Object tracking in cislunar space has become an area of interest within many communities where cislunar space domain awareness (SDA) is critical to operations. Due to the influence of both the Earth and Moon on objects in this domain, the classical two body problem does not accurately describe the dynamics of the state. Legacy tracking capabilities fall short in providing accurate state estimates due to the large volume of space and the highly non-linear dynamics involved. In order to advance SDA in cislunar space, tracking capabilities must be updated for this domain. </p><p dir="ltr">Both the Extended Kalman Filter (EKF) and Gaussian Mixture Extended Kalman Filter (GM-EKF) are used for orbit determination in this thesis along side the Circular Restricted Three Body Problem (CR3BP) to model the non-linear dynamics. The filters are utilized to determine the best estimate of the state as well as its covariance. The two filter's performances are compared to highlight areas in which the assumptions surrounding the EKF are violated resulting in failed tracking, as well as to highlight the power of the GM-EKF for non-linear systems using splitting and merging techniques. </p><p dir="ltr">This thesis presents single and multiple object tracking of objects in a multitude of cislunar orbits using a Moon ground-based sensor. Multiple object tracking is accomplished using a novel Lyapunov-based scheduler in order to reduce the total system uncertainty. The environment is modeled to include exclusion zones which preclude measurements. These zones consist of conjunction from the Earth and Sun, brightness constraints, and camera field of regard (FOR). When measurements are unavailable the uncertainty in the state estimation rises significantly.</p><p dir="ltr">An investigation of varied sensor placements and Sun-Earth-Moon geometries provides results to inform locations and trends which are able to confidently track both single and multiple objects in cislunar orbits. </p>

A face tracker is exactly what it sounds like. It tracks a face in a video sequence. Depending on the complexity of the tracker, it could track the face as a rigid object or as a complete deformable face model with face expressions. This report is based on the work of a real time feature based face tracker. Feature based means that you track certain features in the face, like points with special characteristics. It might be a mouth or eye corner, but theoretically it could be any point. For this tracker, the latter is of interest. Its task is to extract global parameters, i.e. rotation and translation, as well as dynamic facial parameters (expressions) for each frame. It tracks feature points using motion between frames and a textured face model (Candide). It then uses an extended Kalman filter to estimate the parameters from the tracked feature points.

abstract: For the last 50 years, oscillator modeling in ranging systems has received considerable attention. Many components in a navigation system, such as the master oscillator driving the receiver system, as well the master oscillator in the transmitting system contribute significantly to timing errors. Algorithms in the navigation processor must be able to predict and compensate such errors to achieve a specified accuracy. While much work has been done on the fundamentals of these problems, the thinking on said problems has not progressed. On the hardware end, the designers of local oscillators focus on synthesized frequency and loop noise bandwidth. This does nothing to mitigate, or reduce frequency stability degradation in band. Similarly, there are not systematic methods to accommodate phase and frequency anomalies such as clock jumps. Phase locked loops are fundamentally control systems, and while control theory has had significant advancement over the last 30 years, the design of timekeeping sources has not advanced beyond classical control. On the software end, single or two state oscillator models are typically embedded in a Kalman Filter to alleviate time errors between the transmitter and receiver clock. Such models are appropriate for short term time accuracy, but insufficient for long term time accuracy. Additionally, flicker frequency noise may be present in oscillators, and it presents mathematical modeling complications. This work proposes novel H∞ control methods to address the shortcomings in the standard design of time-keeping phase locked loops. Such methods allow the designer to address frequency stability degradation as well as high phase/frequency dynamics. Additionally, finite-dimensional approximants of flicker frequency noise that are more representative of the truth system than the tradition Gauss Markov approach are derived. Last, to maintain timing accuracy in a wide variety of operating environments, novel Banks of Adaptive Extended Kalman Filters are used to address both stochastic and dynamic uncertainty. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2020

La commande sans capteur mécanique de la machine synchrone à aimants permanents est un sujet largement répandu dont les plus grandes difficultés connues, quel que soit l’observateur utilisé, sont celui du démarrage à vitesse nulle et plus largement de la commande aux basses vitesses, et celui du rejet des fortes perturbations du couple. Afin d’y faire obstacle, diverses adaptations des algorithmes d’observateur ont été proposées afin d’améliorer le comportement de la machine en commande sans capteur. Par ailleurs, il est couramment admis que les déchets de tension produit par l’onduleur sont nuisibles à l’observation de la position, les tensions de référence étant légèrement différentes des tensions appliquées aux enroulements de la machine. Quelques propositions apparaissent dans certaines publications notamment en établissant un algorithme de compensation. C’est ainsi que les travaux de cette thèse portent sur cette thématique, celle d’améliorer la commande sans capteur dans les situations d’observabilité difficile en proposant une solution originale afin de faire correspondre au mieux les tensions appliquées à la machine et les tensions de référence utiles à l’observateur. Les résultats montrent que la solution proposée et largement analysée améliore considérablement le comportement de la machine en commande aux basses vitesses et en rejet de perturbation, tant en régime permanent qu’en régime transitoire ; une analyse de Fourier des courants mesurés atteste l’efficacité de la méthode et une analyse des grandeurs observées par la statistique descriptive met en lumière l’intérêt de l’algorithme. Nous montrons ainsi que la solution proposée permet d’observer la vitesse et la position en deçà de la vitesse mécanique de 15 rad/s alors que la commande est instable en deçà de 20 rad/s quand la solution n’est pas mise en œuvre. Un constat similaire apparaît en rejet de perturbation. D’autres résultats montrent que l’observation à plus basse vitesse est entachée d’une perturbation liée à un couple pulsatoire dont l’origine peut être le couple de détente, lequel n’est pas pris en compte par le modèle de la machine. / The sensorless control of the permanent magnet synchronous machine is a subject widely spread. Two great difficulties are known; (i) the start at nil initial speed and more generally the control at very low speed whatever the observer used and (ii) the high torque disturbance rejection. In order to hinder these difficulties numerous modifications of observer algorithms were proposed to improve the performances of the permanent magnet synchronous machine sensorless control. Moreover, we admit commonly that the drop voltages due to the inverter are prejudicial to the position estimated, because the difference between the voltage reference transmitted to the PWM (pulse width modulation) and the motor winding voltage is not negligible at low speed and low load torque. According to the literature, several papers propose some solutions by compensation algorithms and voltage estimator in particular. So, the goal of this thesis is to estimate the winding voltage and to apply the state observer by Extended Kalman Filtering to improve more finely the sensorless control. We propose an original solution to estimate the voltage references which is applied to the observer. Numerous experimental results show the attractive effects in marked contrast to the sensorless control results without estimation of the winding voltages. The results of sensorless control show that the solution proposed which widely analysed improves significantly the estimation errors of the motor running in low speed range and low torque disturbances range. Fourier analyses and statistic data obtained in steady state speed and results during the transient response indicate complementary results and highlight the interest of the estimation algorithm. Our study brings out that the estimation error reduction allows to running the motor at mechanical speed short of 15 rad/s. In the other hand, the system is instable with speed short of 20 rad/s if the voltage references are used by the observer rather than the estimation voltages. The same improvement appears in disturbance rejection. Other results show that the estimated position errors at lower speed increases in spite of the estimation algorithm. In fact, the torque disturbances are dominant at low speed, low load torque and are harmful to control the electromagnetic torque.

Covariance Matrix in target tracking algorithms has a critical role at multi- sensor track fusion systems. This matrix reveals the uncertainty of state es- timates that are obtained from diferent sensors. So, many subproblems of track fusion usually utilize this matrix to get more accurate results. That is why this matrix should be interchanged between the nodes of the multi-sensor tracking system. This thesis mainly deals with analysis of approximations of the covariance matrix that can best represent this matrix in order to efectively transmit this matrix to the demanding site. Kullback-Leibler (KL) Distance is exploited to derive some of the representations for Gaussian case. Also com- parison of these representations is another objective of this work and this is based on the fusion performance of the representations and the performance is measured for a system of a 2-radar track fusion system.