Browsing by Subject "Extended Kalman Filter"

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Estimation for spacecraft docking with a known target
(2019-05) Bell, James Taggart; Zanetti, Renato, 1978-; Akella, Maruthi
The problem of autonomous docking in space is difficult, open, and increasingly important. In the case of this report, a chaser spacecraft attempting to dock with the International Docking System Standard on a target craft is analyzed. After motivating the need for augmenting the relative position and orientation estimates with the estimates of their rates, an Extended Kalman Filter is derived to estimate these states with LIDAR and accelerometer measurements. The main assumptions for this report are that the target be in a nearly circular orbit and that the chaser start in near proximity of the target and have access to its own orientation. The filter is shown to work along a predetermined trajectory with both deadbeat control informed by the true state and a closed-loop feedback controller informed by the estimated state
Navigation filter design and comparison for Texas 2 STEP nanosatellite
(2009-12) Wright, Cinnamon Amber; Lightsey, E. Glenn; Bishop, Robert H.
A Discrete Extended Kalman Filter has been designed to process measurements from a magnetometer, sun sensor, IMU, and GPS receiver to provide the relative position, velocity, attitude, and gyro bias of a chaser spacecraft relative to a target spacecraft. An Extended Kalman Filter with Uncompensated Bias has also been developed for the implementation of well known biases and errors that are not directly observable. A detailed explanation of the algorithms, models, and derivations that go into both filters is presented. With this simulation and specific sensor selection the position of the chaser spacecraft relative to the target can be estimated to within about 5 m, the velocity to within .1 m/s, and the attitude to within 2 degrees for both filters. If a thrust is applied to the IMU measurements, it takes about 1.5 minutes to get a good position estimate, using the Extended Kalman Filter with Uncompensated Bias. The error settles almost immediately using the general Extended Kalman Filter. These filters have been designed for and can be implemented on almost any small, low cost, low power satellite with this inexpensive set of sensors.
Optical navigation: comparison of the extended Kalman filter and the unscented Kalman filter
(2009-08) McFerrin, Melinda Ruth; Bishop, Robert H., 1957-; Akella, Maruthi Ram, 1972-
Small satellites are becoming increasingly appealing as technology advances and shrinks in both size and cost. The development time for a small satellite is also much less compared to a large satellite. For small satellites to be successful, the navigation systems must be accurate and very often they must be autonomous. For lunar navigation, contact with a ground station is not always available and the system needs to be robust. The extended Kalman filter is a nonlinear estimator that has been used on-board spacecraft for decades. The filter requires linear approximations of the state and measurement models. In the past few years, the unscented Kalman filter has become popular and has been shown to reduce estimation errors. Additionally, the Jacobian matrices do not need to be derived in the unscented Kalman filter implementation. The intent of this research is to explore the capabilities of the extended Kalman filter and the unscented Kalman filter for use as a navigation algorithm on small satellites. The filters are applied to a satellite orbiting the Moon equipped with an inertial measurement unit, a sun sensor, a star camera, and a GPS-like sensor. The position, velocity, and attitude of the spacecraft are estimated along with sensor biases for the IMU accelerometer, IMU gyroscope, sun sensor and star camera. The estimation errors are compared for the extended Kalman filter and the unscented Kalman filter for the position, velocity and attitude. The analysis confirms that both navigation algorithms provided accurate position, velocity and attitude. The IMU gyroscope bias was observable for both filters while only the IMU accelerometer bias was observable with the extended Kalman filter. The sun sensor biases and the star camera biases were unobservable. In general, the unscented Kalman filter performed better than the extended Kalman filter in providing position, velocity, and attitude estimates but requires more computation time.
Recursive estimation of Systemic Vascular Resistance using measurements from a left ventricular assist device
(2019-05) Pawar, Suraj Rajendra; Longoria, Raul G.
Cardiovascular disease is the leading cause of deaths worldwide, and one of the ways to treat patients with congestive heart failure is to perform a heart transplant. As the demand for this procedure rises, the disproportionate availability of suitable donors needs to be countered with ways to care and sustain patients who are waiting for a transplant. In this regard, the use of left ventricular assist devices (LVAD) has increased. The research conducted in this Thesis is primarily concerned with the TORVAD [superscript TM] (Windmill Cardiovascular Systems In., Austin , TX), a rotary blood pump type LVAD. The load faced by the left ventricle during ejection of blood is termed as Systemic Vascular Resistance (SVR), and is an important parameter that can indicate cardiovascular health. Abnormalities in SVR have been found to be a good indicator of hypertension, heart failure, shock and sepsis. A consistently low SVR can even be a predictor of mortality. The goal of this Thesis is to investigate ways of recursively estimating SVR in a patient, by using measurements that the TORVAD [superscript TM] provides. The Extended Kalman Filter is used to develop an estimation algorithm based on a reduced order model of the cardiovascular system. The estimation accuracy of the algorithm is tested by generating data through simulations of a computational model of the cardiovascular system, and by collecting measurements from the TORVAD [superscript TM] while it operates in a mock circulation loop. The algorithm is found to estimate SVR satisfactorily using the available measurements, and is robust to different initial conditions.
Vehicle-terrain parameter estimation for small-scale robotic tracked vehicle
(2010-12) Dar, Tehmoor Mehmoud; Longoria, Raul G.; Fahrenthold, Eric; Bryant, Michael D.; Fernandez, Benito; Wang, Junmin
Methods for estimating vehicle-terrain interaction parameters for small scale robotic vehicles have been formulated and evaluated using both simulation and experimental studies. A model basis was developed, guided by experimental studies with an iRobot PackBot. The intention was to demonstrate whether a nominally instrumented robotic vehicle could be used as a test platform for generating data for vehicle-terrain parameter estimation. A comprehensive skid-steered model was found to be sensitive enough to distinguish between various forms of unknown terrains. This simulation study also verified that the Bekker model for large scale vehicles adopted for this research was applicable to the small scale robotic vehicle used in this work. This fact was also confirmed by estimating coefficients of friction and establishing their dependence on forward velocity and turning radius as the vehicle traverses different terrains. On establishing that mobility measurements for this robotic were sufficiently sensitive, it was found that estimates could be made of key dynamic variables and vehicle-terrain interaction parameters. Four main contributions are described for reliably and robustly using PackBot data for vehicle-terrain property estimation. These estimation methods should contribute to efforts in improving mobility of small scale tracked vehicles on uncertain terrains. The approach is embodied in a multi-tiered algorithm based on the dynamic and kinematic models for skid-steering as well as tractive force models parameterized by key vehicle-terrain parameters. In order to estimate and characterize the key parameters, nonlinear estimation techniques such as the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and a General Newton Raphson (GNR) method are integrated into this multi-tiered algorithm. A unique idea in using an EKF with an added State Noise Compensation algorithm is presented which shows its robustness and consistency in estimating slip variables and other parameters for deformable terrains. In the multi-tiered algorithm, a kinematic model of the robotic vehicle is used to estimate slip variables and turning radius. These estimated variables are stored in a truth table and used in a skid-steered dynamic model to estimate the coefficients of friction. The total estimated slip on the left and right track, along with the total tractive force computed using a motor model, are then used in the GNR algorithm to estimate the key vehicle-terrain parameters. These estimated parameters are cross-checked and confirmed with EKF estimation results. Further, these simulation results verify that the tracked vehicle tractive force is not dependent on cohesion for frictional soils. This sequential algorithm is shown to be effective in estimating vehicle-terrain interaction properties with relatively good accuracy. The estimated results obtained from UKF and EKF are verified and compared with available experimental data, and tested on a PackBot traversing specified terrains at the Southwest Research Institute (SwRI), Small Robotics Testbed in San Antonio, Texas. In the end, based on the development and evaluation of small scale vehicle testing, the effectiveness of on-board sensing methods and estimation techniques are also discussed for potential use in real time estimation of vehicle-terrain parameters.