Browsing by Subject "Kalman Filtering"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Displacement Monitoring of I-35W Saint Anthony Falls Bridge with Current Vibration-Based System
    (Minnesota Department of Transportation, 2019-01) Brown, Riley J; Gaebler, Karl O; Shield, Carol K; Linderman, Lauren E
    Since the opening of the I-35W Saint Anthony Falls Bridge in 2008, over 500 sensors have been collecting data to better understand the behavior of post-tensioned concrete box girder structures. Recent research in the accelerometers installed on the bridge indicates they can be effectively used in a vibration-based structural health monitoring system, but previous studies have shown that natural frequency alone may not be sufficient to determine the performance of the structure. Vertical displacements were believed to be a simpler performance measure as direct comparisons can be made with design calculations and maintenance guidelines. To avoid the shortcomings of conventional displacement measurement options, this study focuses on using the currently installed accelerometers to estimate the vertical displacements of the southbound bridge. The proposed technique utilizes up-to-date modal parameters within a dual Kalman filter to estimate the vertical displacements of the structure from noisy acceleration measurements. When applied to the I-35W Saint Anthony Falls Bridge, it was found that the dual Kalman filter approach captures only dynamic displacements due to relatively slow loading (e.g., traffic loading and thermal loading) and the corresponding low-frequency static displacements are likely too small for GPS measurements due to the high stiffness of the structure.
  • Thumbnail Image
    Item
    Real Time Angle Of Attack Estimation For The Hycube Flight Vehicle In Gps-Denied Re-Entry
    (2024-01) Vedvik, Sophia
    With global interests in the study of hypersonic flow, large research efforts have gone towards collecting statistically significant amounts of high speed flow data at low costs. CubeSats are proving to be an economical testing platform for a variety of scientific experiments, where valuable hypersonic data can be collected and relayed upon re-entry to Earth. However, due to the budget, volume, and power constraints of CubeSats, many of the on-board sensors, including inertial measurement units (IMUS), have decreased accuracy. For purposeful data collection to occur, the sensors on-board typically work in conjunction with robust synthetic air data algorithms. To back out useful data on the vehicle's response during re-entry, the angle of attack of the vehicle must be estimated with one of such algorithms. This work proposes using an Extended Kalman Filter (EKF) which fuses an attitude determination algorithm with low-grade IMU angular rates and measurements of Earth's magnetic field. But in the case of re-entry, the vehicle will become deprived of Global Positioning System (GPS) data, which is required to obtain estimates of the Earth's magnetic field that work in conjunction with magnetometer magnetic field measurements. Thus, after developing the EKF framework, this work will perform a trade study to analyze ways in which Earth's magnetic field can still be a viable method to aid low-grade IMU attitude estimates. The trade study environment is modeled after the Hypersonic Configurable Unit Ballistic Experiment (HyCUBE), a project in development at the University of Minnesota that is leveraging the CubeSat form factors to collect valuable hypersonic flow data upon re-entry. Future work and improvements to the EKF, as well as the impact of this work will then be discussed.
  • Thumbnail Image
    Item
    Single-Vector Aiding of an IMU for CubeSat Attitude Determination
    (2020-07) Laughlin, Kail
    This thesis examines CubeSat attitude determination using the Earth’s magnetic field (EMF) vector aiding a low-cost IMU. CubeSats provide relatively cost-effective methods of performing scientific research in orbital environments. However, to adequately perform this research, knowledge of the CubeSat’s orientation in 3D space (attitude) is often required. To that end, the design of a reliable attitude determination (AD) system on-board a CubeSat is a critical aspect for many mission designers. As a primary goal of CubeSat design is to ensure science objectives are met while minimizing, cost, mass, and volume, this thesis investigates a minimal sensor approach to CubeSat AD. Specifically, an inertial based AD scheme reliant on the use of an inertial measurement unit (IMU) aided only by vector measurements of the Earth’s magnetic field (EMF) is developed. An extended Kalman filter (EKF) approach to recursively estimate the attitude on-orbit using an IMU and a three-axis magnetometer (TAM) is detailed. Additionally, we describe a test to assess the stochastic observability of the EKF developed. We present simulation results showcasing the performance of the AD system for multiple orbital inclinations and initial attitude errors. Moreover, we discuss conditions in which the EMF vector can and cannot be effectively utilized as the sole aiding measurement, and we evaluate the stochastic observability of the linearized discrete time system. We extend the AD system discussed here to two current University of Minnesota Small Satellite Research Lab CubeSat designs: IMPRESS and EXACT. We describe future work for the implementation of the AD system and potential improvements to the EKF design.