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Kalman Filter Implementation to Determine Orbit and Attitude of a Satellite in a Molniya OrbitKeil, Elizabeth Marie 23 June 2014 (has links)
This thesis details the development and implementation of an attitude and orbit determining Kalman filter algorithm for a satellite in a Molniya orbit. To apply the Kalman Filter for orbit determination, the equations of motion of the two body problem were propagated using Cowell's formulation. Four types of perturbing forces were added to the propagated model in order to increase the accuracy of the orbit prediction. These four perturbing forces are Earth oblateness, atmospheric drag, lunar gravitational forces and solar radiation pressure. Two cases were studied, the first being the implementation of site track measurements when the satellite was over the ground station. It is shown that large errors, upwards of ninety meters, grow as time from last measurement input increases. The next case studied was continuous measurement inputs from a GPS receiver on board the satellite throughout the orbit. This algorithm greatly decreased the errors seen in the orbit determining algorithm due to the accuracy of the sensor as well as the continuous measurement inputs throughout the orbit. It is shown that the accuracy of the orbit determining Kalman filter also depends on the length of time between each measurement update. The errors decrease as the time between measurement updates decreases. Next the Kalman filter is applied to determine the satellite attitude. The rotational equations of motion are propagated using Cowell's Formulation and numerical integration. To increase the fidelity of the model four disturbing torques are included in the rotational equations of motion model: gravity gradient torque, solar pressure torque, magnetic torque, and aerodynamic torque. Four cases were tested corresponding to four different on board attitude determining sensors: magnetometer, Earth sensor, sun sensor, and star tracker. A controlled altitude path was chosen to test the accuracy of each of these cases and it was shown that the algorithm using star tracker measurements was three hundred times more accurate than that of the magnetometer algorithm. / Master of Science
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Online parameter estimation applied to mixed conduction/radiationShah, Tejas Jagdish 29 August 2005 (has links)
The conventional method of thermal modeling of space payloads is expensive and cumbersome. Radiation plays an important part in the thermal modeling of space payloads because of the presence of vacuum and deep space viewing. This induces strong nonlinearities into the thermal modeling process. There is a need for extensive correlation between the model and test data. This thesis presents Online Parameter Estimation as an approach to automate the thermal modeling process. The extended
Kalman fillter (EKF) is the most widely used parameter estimation algorithm for
nonlinear models. The unscented Kalman filter (UKF) is a new and more accurate technique for parameter estimation. These parameter estimation techniques have been evaluated with respect to data from ground tests conducted on an experimental space payload.
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Airspeed estimation of aircraft using two different models and nonlinear observersRoser, Alexander, Thunberg, Anton January 2023 (has links)
When operating an aircraft, inaccurate measurements can have devastating consequences. For example, when measuring airspeed using a pitot tube, icing effects and other faults can result in erroneous measurements. Therefore, this master thesis aims to create an alternative method which utilizes known flight mechanical equations and sensor fusion to create an estimate of the airspeed during flight. For validation and generation of flight data, a simulation model developed by SAAB AB, called ARES, is used. Two models are used to describe the aircraft behavior. One of which is called the dynamic model and utilizes forces acting upon the aircraft body in the equations of motion. The other model, called the kinematic model, instead describes the motion with accelerations of the aircraft body. The measurements used are the angle of attack (AoA), side-slip angle (SSA), GPS velocities, and angular rates from an inertial measurement unit (IMU). The dynamic model assumes that engine thrust and aerodynamic coefficients are already estimated to calculate resulting forces, meanwhile the kinematic model instead uses body fixed accelerations from the IMU. These models are combined with filters to create estimations of the airspeed. The filters used are the extended Kalman filter (EKF) and unscented Kalman filter (UKF). These are combined with the two models to create in total four methods to estimate the airspeed. The results show no major difference in the performance between the filters except for computational time, for which the EKF has the fastest. Further, the result show similar airspeed estimation performance between the models, but differences can be seen. The kinematic model manages to estimate the wind with higher details and to converge faster, compared to the dynamic model. Both models suffer from an observability problem. This problem entails that the aircraft needs to be maneuvered to excite the AoA and SSA in order for the estimation methods to evaluate the wind, which is crucial for accurate airspeed estimation. The robustness of the dynamic model regarding errors in engine thrust and aerodynamic coefficients are also researched, which shows that the model is quite robust against errors in these values.
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Modelling compositional time series from repeated surveysNascimento Silva, Denise Britz do January 1996 (has links)
No description available.
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GPS assisted stabilization and tracking for a camera systemJohansson, Hugo, Kjellström, Hendric January 2017 (has links)
Today in most vehicles in battle, a camera system is used to manually lock a target and maintaining visual of the target as the vehicle is moving. In order to simplify this, this thesis investigates the approach to semi-automate the process by first manually locking the target and then let the camera approximate the trajectory of the enemy vehicle. In this thesis, the enemy vehicle is not moving. The ability to provide a truthful simulation environment for testing is crucial and will be discussed in this thesis along with three different estimators derived from the Kalman filter. Parameter identification and dynamic modelling of the camera are also presented that serves as a basis for the part of automatic control and for the experiments on the hardware. The simulation environment gave promising results when locating the target based on angle and radius estimation. By simulating a human operator, big deviations from the true value was no longer a problem since its purpose is to take over and steer the camera to the correct value. After gathering results from the simulations, Model-Based Design made it possible to test the algorithms in real life. The biggest challenge was to produce lifelike motions to test the hardware on and therefore made it harder to conclude the end result for the experiments carried out by the hardware on the moving platform.
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ESSAYS IN OPTIMAL MONETARY POLICY AND STATE-SPACE ECONOMETRICSScott, C. Patrick January 1900 (has links)
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).
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Interactive Planning and Sensing for Aircraft in Uncertain Environments with Spatiotemporally Evolving ThreatsCooper, Benjamin S 30 November 2018 (has links)
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.
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A Fast and Robust Image-Based Method for tracking Robot-assisted Needle Placement in Real-time MR ImagesJanga, Satyanarayana Reddy 15 January 2014 (has links)
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.
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Essays on the credit default swap marketWang, Peipei, Banking & Finance, Australian School of Business, UNSW January 2009 (has links)
The focus of this dissertation is the European Credit Default Swaps (CDSs) market. CDSs are the most popular credit derivative products. Three issues are discussed, the first, which is covered in chapter 2, is the investigation of non-diversifiable jump risk in iTraxx sector indices based on a multivariate model that explicitly admits discrete common jumps for an index and its components. Our empirical research shows that both the iTraxx Non-Financials and their components experience jumps during the sample period, which means that the jump risks in the iTraxx sector index are not diversifiable. The second issue, which is covered in chapter 3 is the component structure of credit default swap spreads and their determinants. We firstly extract a transitory component and a persistent component from two different maturities of the Markit iTraxx index and then regress these components against proxies for several commonly used explanatory variables. Our results show that these explanatory variables have significant but differing impacts on the extracted components, which indicates that a two-factor formulation may be needed to model CDS options. The last issue, which is covered in chapters 4, 5 and 6 is the investigation of the linkage between the credit default swap market and the equity market within the European area. We innovatively calibrate the CDS option with the Heston Model to get the implied volatility in the CDS market, which allows us to investigate both the characteristic of implied volatility in the CDS market and the relationship of the two markets not only on the level of daily changes but also with regard to its second moment. Our analysis shows that the stock market weakly leads the CDS market on daily changes but for implied volatility, the stock market leads the CDS market. A VECM analysis shows that only the stock market contributes to price discovery. For sub-investment grade entities, the interactivities between the implied volatility of the CDS market and the implied volatility of the stock market are stronger, especially during the recent credit crunch period. All these results have important implications for the construction of portfolios with credit-sensitive instruments.
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Research and Tutorial ExpositionStrang, Gilbert 01 1900 (has links)
My research is concentrated on applications of linear algebra in engineering, including wavelet analysis and structured matrices and (currently) approximation of large dense matrices by a mosaic of low rank blocks. / Singapore-MIT Alliance (SMA)
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