Spinning targets are usually observed in videos. The targets may sometimes appear as mobile targets at the same time. The targets become mobile spinning targets. Tracking a single point on a target is easier than tracking the whole target. We use a characteristic point on the target to estimate the interested parameters, such as angular velocity, virtual rotation center and moving velocity. Among these parameters, virtual rotation center does not spin, therefore it can be used to represent the position of the target. Traditionally, extended Kalman filter (EKF), unscented Kalman filter (UKF) and particle filter (PF) are choices for solving the nonlinear problems, but some problems exist. Linearization errors cause that EKF cannot accurately estimate the angular velocity. UKF and PF have high computational complexity. In the thesis, we give angular velocity an initial value. So we can establish a linear dynamic system model to displace the nonlinear model. Then, a new structure is proposed to avoid errors caused by initial value of angular velocity. In the structure, angular velocity is estimated individually and used to correct the initial value by feedback. We try to use fast Fourier transform to estimate angular velocity. But the convergence time of this method is affected by the value of angular velocity, and the direction of angular velocity can not be estimated directly. Therefore, Kalman filter (KF) with pseudo measurement is proposed to estimate the value of angular velocity. The estimator is accurate and has low computational complexity. Once angular velocity is estimated, we can easily predict the virtual rotation center from geometric relationship. In video system, measurements may be quantized and targets may sometimes be obstacled. We fix the measurement equation and use KF to mitigate quantization error. When measurements for the target is missing, the previous state is used to predict the current state. Finally, computer simulations are conducted to verify the effectiveless of the proposed method. The method can work in environments where measurement noise or quantization error exists. The methods can also be applied to different kinds of mobile spinning targets.

Ray theory plays an important role in seismic imaging and velocity model building. Although rays are the high-frequency asymptotic solutions of the wave equation and therefore do not usually capture all details of the wave physics, they provide a convenient and effective tool for a wide range of geophysical applications. Especially, ray theory gives rise to traveltimes. Even though wave-based methods for imaging and model building had attracted significant attentions in recent years, traveltime-based methods are still indispensable and should be further developed for improved accuracy and efficiency. Moreover, there are possibilities for new ray theoretical methods that might address the difficulties faced by conventional traveltime-based approaches. My thesis consists of mainly four parts. In the first part, starting from the linearized eikonal equation, I derive and implement a set of linear operators by upwind finite differences. These operators are not only consistent with fast-marching eikonal solver that I use for traveltime computation but also computationally efficient. They are fundamental elements in the numerical implementations of my other works. Next, I investigate feasibility of using the double-square-root eikonal equation for near surface first-break traveltime tomography. Compared with traditional eikonal-based approach, where the gradient in its adjoint-state tomography neglects information along the shot dimension, my method handles all shots together. I show that the double-square-root eikonal equation can be solved efficiently by a causal discretization scheme. The associated adjoint-state tomography is then realized by linearization and upwind finite-differences. My implementation does not need adjoint state as an intermediate parameter for the gradient and therefore the overall cost for one linearization update is relatively inexpensive. Numerical examples demonstrate stable and fast convergence of the proposed method. Then, I develop a strategy for compressing traveltime tables in Kirchhoff depth migration. The method is based on differentiating the eikonal equation in the source position, which can be easily implemented along with the fast-marching method. The resulting eikonal-based traveltime source-derivative relies on solving a version of the linearized eikonal equation, which is carried out by the upwind finite-differences operator. The source-derivative enables an accurate Hermite interpolation. I also show how the method can be straightforwardly integrated in anti-aliasing and Kirchhoff redatuming. Finally, I revisit the classical problem of time-to-depth conversion. In the presence of lateral velocity variations, the conversion requires recovering geometrical spreading of the image rays. I recast the governing ill-posed problem in an optimization framework and solve it iteratively. Several upwind finite-differences linear operators are combined to implement the algorithm. The major advantage of my optimization-based time-to-depth conversion is its numerical stability. Synthetic and field data examples demonstrate practical applicability of the new approach. / text

Multiazimuth seismic data contains information about how the Earth’s seismic response changes with azimuthal direction. Directional-dependence of the seismic response can be caused by anisotropy or heterogeneity, associated with subsurface features such as fractures, stresses, or structure. Characterizing azimuthal variations is done through velocity analysis, which provides a link between an acquired data set and its image, as well as between the image and subsurface geology. At the stage which conventional velocity analysis is applied, it is difficult to distinguish the geologic cause of observed azimuthal velocity variations. The inability to distinguish the similar effects of anisotropy and heterogeneity leads to positioning errors in the final image and velocity estimates. Regardless of the cause, azimuthally variable velocities require at least three parameters to characterize, as opposed to the conventional single-parameter isotropic velocity. The semblance scan is the conventional tool for seismic velocity analysis, but it was designed for the isotropic case. For multiple parameters, the semblance scan becomes computationally impractical. In order to help address the xiissues of geologic ambiguity and computational efficiency, I develop three methods for multiazimuth seismic velocity analysis based on “velocity-independent” imaging techniques. I call this approach, velocity analysis by velocity-independent imaging, where I reverse the conventional order of velocity estimation followed by image estimation. All three methods measure time-domain effective-velocity parameters. The first method, 3D azimuthally anisotropic velocity-independent NMO, replaces the explicit measurement of velocity with local slope detection. The second method, time-warping, uses local slope information to predict traveltime surfaces without any moveout assumption beforehand, and then fit them with a multiparameter velocity model. The third method, azimuthal velocity continuation, uses diffraction image focusing as a velocity analysis criterion, thereby performing imaging and velocity analysis simultaneously. The first two methods are superior to the semblance scan in terms of computational efficiency and their ability to handle multi-parameter models. The third method is similar to a single multi-parameter semblance scan in computational cost, but it helps handle the ambiguity between structural heterogeneity and anisotropy, which leads to better positioned images and velocity estimates. / text

In this thesis, development of a control system is aimed for a 1 DOF haptic device, namely Haptic Box. Besides, it is also constructed. Haptic devices are the manipulators that reflect the interaction forces with virtual or remote environments to its users. In order to reflect stiffness, damping and inertial forces on a haptic device position, velocity and acceleration measurements are required. The only motion sensor in the system is an incremental optical encoder attached to the back of the DC motor. The encoder is a good position sensor but velocity and acceleration estimations from discrete position and time data is a challenging work. To estimate velocity and acceleration some methods in the literature are employed on the Haptic Box and it is concluded that Kalman filtering gives the best results. After the velocity and acceleration estimations are acquired haptic control algorithms are tried experimentally. Finally, a virtual environment application is presented.

京都大学 / 0048 / 新制・課程博士 / 博士(人間健康科学) / 甲第19970号 / 人健博第38号 / 新制||人健||3(附属図書館) / 33066 / 京都大学大学院医学研究科人間健康科学系専攻 / (主査)教授 杉本 直三, 教授 精山 明敏, 教授 黒田 知宏 / 学位規則第4条第1項該当 / Doctor of Human Health Sciences / Kyoto University / DFAM

The project explores the possibilities on how to improve the accuracy of GPS velocity data by using sensor fusion with an extended Kalman filter. The proposed solution in this project is a sensor fusion between the GPS and IMU of the system, where the extended Kalman filter was used to estimate the velocity from the sensor data. The hardware used for the data acquisition to the proposed solution was a Pixhawk 4 (PX4), which has an IMU consisting of accelerometers, gyroscopes and magnetometers. The PX4:s corresponding GPS module was also used to collect accurate velocity data. The data was logged using Simulink and later processed with MATLAB. The sensor fusion using the extended Kalman filter gave good estimates upon constant acceleration but had problems with estimating over varying acceleration. This was initially planned to be solved using smoothing filters, which is an essential part of the fusion process, but was never implemented due to time constraints. The constructed filter acts as a foundation towards future improvement. Other methods such as unscented Kalman filter, particle filter and neural network could also be explored to improve the estimation of the velocity due to these filters being known to have better performance. However, most of these alternatives need more computing power and are generally harder to implement compared to the extended Kalman filter. This project would be beneficial to QTAGG, since increasing the velocity resolution and accuracy of the system can provide possibilities of better optimization. It is also a commonly implemented solution where there are many state of the art implementations available.

This dissertation is focused on developing the new target localization and the target velocity estimation methods for frequency-only multi-input, multi-output (MIMO) radar systems with widely separated antennas. If the frequency resolutions of the transmitted signals are enough, only the received frequencies and the Doppler shifts can be used to find the position of the target. In order to estimate the position and the velocity of the target, most multistatic radars or radar networks use multiple independent measurements from the target such as time-of-arrival (TOA), angle-of-arrival (AOA) and frequency-of-arrival (FOA). Although, frequency based systems have many advantages, frequency based target localization methods are very limited in literature because of the fact that highly non-linear equations are involved in solutions. In this thesis, alternative target localization and the target velocity estimation methods are proposed for frequency-only systems with low complexity. One of the proposed methods is able to estimate the target position and the target velocity based on the measurements of the Doppler frequencies. Moreover, the target movement direction can be estimated efficiently. This method is referred to as &quot / Target Localization via Doppler Frequencies - TLDF&quot / and it can be used for not only radar but also all frequency-based localization systems such as Sonar or Wireless Sensor Networks. Besides the TLDF method, two alternative target position estimation methods are proposed as well. These methods are based on the Doppler frequencies, but they requires the target velocity vector to be known. These methods are referred to as &quot / Target Localization via Doppler Frequencies and Target Velocity - TLD&amp / V methods&quot / and can be divided two sub-methods. One of them is based on the derivatives of the Doppler Frequencies and hence it is called as &quot / Derivated Doppler - TLD&amp / V-DD method&quot / . The second method uses the Maximum Likelihood (ML) principle with grid search, hence it is referred to as &quot / Sub-ML, TLD&amp / V-subML method&quot / . The more realistic signal model for ground based, widely separated MIMO radar is formed as including Swerling target fluctuations and the Doppler frequencies. The Cramer-Rao Bounds (CRB) are derived for the target position and the target velocity estimations for this signal model. After the received signal is constructed, the Doppler frequencies are estimated by using the DFT based periodogram spectral estimator. Then, the estimated Doppler frequencies are collected in a fusion center to localize the target. Finally, the multiple targets localization problem is investigated for frequency-only MIMO radar and a new data association method is proposed. By using the TLDF method, the validity of the method is simulated not only for the targets which are moving linearly but also for the maneuvering targets. The proposed methods can localize the target and estimate the velocity of the target with less error according to the traditional isodoppler based method. Moreover, these methods are superior than the traditional method with respect to the computational complexity. By using the simulations with MATLAB, the superiorities of the proposed methods to the traditional method are shown.

L'inversion des formes d'onde (full waveform inversion, FWI) a suscité un intérêt dans le monde entier pour sa capacité à estimer de manière précise et détaillée les propriétés physiques du sous-sol. La FWI est généralement formulée sous la forme d'un problème d'ajustement des données par moindres carrés et résolus par une approche linéarisée utilisant des méthodes d'optimisation locales. Cependant, la FWI est bien connue de souffrir du problème de saut de phase rendant les résultats fortement dépendant de la qualité des modèles initiaux. L'inversion des formes d'ondes des arrivées réfléchies (reflection waveform inversion, RWI) a récemment été proposée pour atténuer ce problème en supposant une séparation d'échelle entre le modèle de vitesse lisse et le modèle de réflectivité à haut nombre d'onde. La formulation de RWI considère explicitement les ondes réfléchies afin d'extraire de ces ondes une information sur les variations lisses de vitesse des zones profondes. Cependant, la méthode néglige les ondes transmises qui contraignant les informations lisses de vitesse en proche surface.Dans cette thèse, une étude de la sensibilité en nombre d'ondes des méthodes de FWI et RWI a d'abord été revisitée dans le cadre de la tomographie en diffraction et des décompositions orthogonales. A partir de cette analyse, je propose une nouvelle méthode, à savoir l'inversion jointe des formes d'ondes transmises et réfléchies (joint full waveform inversion, JFWI). La méthode propose une formulation unifiée pour combiner la FWI des transmissions et la RWI pour les réflexions, donnant naturellement une sensibilité commune aux petits nombres d'onde venant des arrivées grand-angle et réfléchies. Les composantes à hauts nombres d'onde sont naturellement atténuées par la formulation. Pour satisfaire l'hypothèse de séparation d'échelle, j'utilise une paramétrisation du sous-sol basée sur la vitesse des ondes de compression et l'impédance acoustique. La complexité temporelle de cette approche est le double de la méthode de FWI classique et la requête mémoire reste la même.Une procédure d'inversion est ensuite proposée, permettant d'estimer alternativement le modèle de la vitesse du sous-sol par JFWI et l'impédance inversion de formes d'ondes réfléchies. Un exemple synthétique réaliste du modèle de Valhall est d'abord utilisé avec des données de streamer et à partir d'un modèle initial très lisse. Dans ce cadre, alors que la FWI converge vers un minimum local, la JFWI réussit à reconstruire un modèle de vitesse lisse de bonne qualité. La prise en compte des ondes tournante par la JFWI montre un fort intérêt pour la qualité de reconstruction superficielle, comparée à la méthode RWI seule. Cela se traduit ensuite par une reconstruction améliorée en profondeur. Le modèle de vitesse lisse construit par JFWI peut ensuite être considéré comme modèle initial pour la FWI classique, afin d'injecter le contenu en haut nombres d'onde tout en évitant le problème de saut de phase.Les avantages et limites de l'approche de JFWI sont ensuite étudiés dans une application sur données réelles, venant d'un profil 2D de données de fond de mer (OBC) recoupant un nuage de gaz au dessus d'un réservoir. Plusieurs modèles initiaux et stratégies d'inversion sont testés afin de minimiser le problème de saut de phase, tout en construisant des modèles de sous-sol avec une résolution suffisante. Sous réserve de mettre en œuvre des stratégies limitant le problème de saut de phase, la JFWI montre qu'elle peut produire un modèle de vitesse acceptable, injectant les bas nombres d'onde dans le modèle de vitesse. L'amélioration de l'éclairage en angles de diffraction fournie par des acquisitions 3D devrait permettre de pouvoir commencer l'inversion par JFWI à partir de modèle encore moins bien définis. / Full waveform inversion (FWI) has attracted worldwide interest for its capacity to estimate the physical properties of the subsurface in details. It is often formulated as a least-squares data-fitting procedure and routinely solved by linearized optimization methods. However, FWI is well known to suffer from cycle skipping problem making the final estimations strongly depend on the user-defined initial models. Reflection waveform inversion (RWI) is recently proposed to mitigate such cycle skipping problem by assuming a scale separation between the background velocity and high-wavenumber reflectivity. It explicitly considers reflected waves such that large-wavelength variations of deep zones can be extracted at the early stage of inversion. Yet, the large-wavelength information of the near surface carried by transmitted waves is neglected.In this thesis, the sensitivity of FWI and RWI to subsurface wavenumbers is revisited in the frame of diffraction tomography and orthogonal decompositions. Based on this analysis, I propose a new method, namely joint full waveform inversion (JFWI), which combines the transmission-oriented FWI and RWI in a unified formulation for a joint sensitivity to low wavenumbers from wide-angle arrivals and short-spread reflections. High-wavenumber components are naturally attenuated during the computation of model updates. To meet the scale separation assumption, I also use a subsurface parameterization based on compressional velocity and acoustic impedance. The temporal complexity of this approach is twice of FWI and the memory requirement is the same.An integrated workflow is then proposed to build the subsurface velocity and impedance models in an alternate way by JFWI and waveform inversion of the reflection data, respectively. In the synthetic example, JFWI is applied to a streamer seismic data set computed in the synthetic Valhall model, the large-wavelength characteristics of which are missing in the initial 1D model. While FWI converges to a local minimum, JFWI succeeds in building a reliable velocity macromodel. Compared with RWI, the involvement of diving waves in JFWI improves the reconstruction of shallow velocities, which translates into an improved imaging at greater depths. The smooth velocity model built by JFWI can be subsequently taken as the initial model for conventional FWI to inject high-wavenumber content without obvious cycle skipping problems.The main promises and limitations of the approach are also reviewed in the real-data application on the 2D OBC profile cross-cutting gas cloud.Several initial models and offset-driven strategies are tested with the aim to manage cycle skipping while building subsurface models with sufficient resolution. JFWI can produce an acceptable velocity model provided that the cycle skipping problem is mitigated and sufficient low-wavenumber content is recovered at the early stage of inversion. Improved scattering-angle illumination provided by 3D acquisitions would allow me to start from cruder initial models.

En övergång till bilar utrustade med avancerade automatiska säkerhetssystem (ADAS) och även utvecklingen mot självkörande fordon innebär ökad trafik på den lokala databussen. Det finns således ett behov av att både minska den faktiska mängden data som överförs, samtidigt som värdet på datat ökas. Data aggregation tillämpas i dagsläget inom områden såsom trådlösasensornätverk och mindre mobila robotar (WMR’s) och skulle kunna vara en del av en lösning. Denna rapport avser undersöka aggregation av sensordata i ett tidskänsligt system. För ett användarfall gällande halka under konvojkörning testas en aggregationsstrategi genom implementation på en fysisk demonstrator. Demonstratorn består av ett autonomt fordon i mindre skala som befinner sig i en konvoj med ett annat identiskt fordon. Resultaten pekar mot att ett viktat medelvärde, som i realtid anpassar sin viktning baserat på specifika sensorers koherens, med fördel kan användas för att estimera fordonshastighet baserat på individuella hjuls sensordata. Därefter kan en slip ratio beräknas, vilket avgör om fordonet befinner sig i ett tillstånd av halka eller ej. Begränsningar för den undersökta strategin inkluderar antalet icke-halkande hjul som behövs för tillförlitliga resultat. Simulerade resultat antyder att extra hastighetsreferenser behövs för tillförlitliga resultat. Relaterat till användarfallet konvojkörning föreslås att andra fordon används som hastighetsreferens. Detta skulle innebära en ökad precision för estimeringen av fordonshastigheten samt utgöra en intressant sammanslagning av områdena samarbetande cyberfysiska system (CO-CPS) och dataaggregation. / With an impending shift to more advanced safety systems and driver assistance (ADAS) in the vehicles we drive, and also increased autonomousity, comes increased amounts of data on the internal vehicle data bus. There is a need to lessen the amount of data and at the same time increase its value. Data aggregation, often applied in the field of environmental sensing or small mobile robots (WMR’s), could be a partial solution. This thesis choses to investigate an aggregation strategy applied to a use case regarding slip detection in a vehicle convoy. The approach was implemented in a physical demonstrator in the shape of a small autonomousvehicle convoy to produce quantitative data. The results imply that a weighted adaptive average can be used for vehicle velocity estimation based on the input of four individual wheel velocities. There after a slip ratio can be calculated which is used to decide if slip exists or not. Limitations of the proposed approach is however the number of velocity references that is needed since the results currently apply to one-wheel slipon a four-wheel vehicle. A proposed future direction related to the use case of convoy driving could be to include platooning vehicles as extra velocity references for the vehicles in the convoy, thus increasing the accuracy of the slip detection and merging the areas of CO-CPS and data aggregation.

Spectroscopic techniques allow for studies of materials and organisms on the atomic and molecular level. Examples of such techniques are nuclear magnetic resonance (NMR) spectroscopy—one of the principal techniques to obtain physical, chemical, electronic and structural information about molecules—and magnetic resonance imaging (MRI)—an important medical imaging technique for, e.g., visualization of the internal structure of the human body. The less well-known spectroscopic technique of nuclear quadrupole resonance (NQR) is related to NMR and MRI but with the difference that no external magnetic field is needed. NQR has found applications in, e.g., detection of explosives and narcotics. The first part of this thesis is focused on detection and identification of solid and liquid explosives using both NQR and NMR data. Methods allowing for uncertainties in the assumed signal amplitudes are proposed, as well as methods for estimation of model parameters that allow for non-uniform sampling of the data. The second part treats two medical applications. Firstly, new, fast methods for parameter estimation in MRI data are presented. MRI can be used for, e.g., the diagnosis of anomalies in the skin or in the brain. The presented methods allow for a significant decrease in computational complexity without loss in performance. Secondly, the estimation of blood flow velo-city using medical ultrasound scanners is addressed. Information about anomalies in the blood flow dynamics is an important tool for the diagnosis of, for example, stenosis and atherosclerosis. The presented methods make no assumption on the sampling schemes, allowing for duplex mode transmissions where B-mode images are interleaved with the Doppler emissions.