Embedded Relative Navigation Sensor Fusion Algorithms for Autonomous Rendezvous and Docking Missions

bd Systems (a subsidiary of SAIC) has developed a suite of embedded relative navigation sensor fusion algorithms to enable NASA autonomous rendezvous and docking (AR&D) missions. Translational and rotational Extended Kalman Filters (EKFs) were developed for integrating measurements based on the vehicles' orbital mechanics and high-fidelity sensor error models and provide a solution with increased accuracy and robustness relative to any single relative navigation sensor. The filters were tested tinough stand-alone covariance analysis, closed-loop testing with a high-fidelity multi-body orbital simulation, and hardware-in-the-loop (HWIL) testing in the Marshall Space Flight Center (MSFC) Flight Robotics Laboratory (FRL)

Vehicle model-based filtering for spacecraft attitude determination

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1998.Includes bibliographical references (p. 166-170) and index.This thesis investigates the use of vehicle model-based filtering for spacecraft attitude determination. Whereas traditional navigation filters typically rely only on the kinematic relations between body rate and attitude in their filter designs, the state estimator presented here expands the plant model to include rigid body effects and disturbance torques. When rate sensing gyroscope measurement error components are large, as is anticipated in the new generation of micromechanical inertial sensors, the model-based approach provides superior performance to the standard kinematic designs. The estimation performance gains, which include enhanced attitude tracking of several tenths of a degree and closed-loop control stabilization, are most apparent when external attitude data becomes sparse. Even if the gyroscope measurement quality were to improve, for some satellite missions the possibility of an external measurement outage still necessitates vehicle dynamic modeling for greater gyro bias observability. The thesis also gives insight into robustness measures to compensate for model uncertainty, disturbance torque estimation, and GPS multipath error mitigation.by Christopher W. Dever.S.M

Estimating short-period dynamics using an extended Kalman filter

An extended Kalman filter (EKF) is used to estimate the parameters of a low-order model from aircraft transient response data. The low-order model is a state space model derived from the short-period approximation of the longitudinal aircraft dynamics. The model corresponds to the pitch rate to stick force transfer function currently used in flying qualities analysis. Because of the model chosen, handling qualities information is also obtained. The parameters are estimated from flight data as well as from a six-degree-of-freedom, nonlinear simulation of the aircraft. These two estimates are then compared and the discrepancies noted. The low-order model is able to satisfactorily match both flight data and simulation data from a high-order computer simulation. The parameters obtained from the EKF analysis of flight data are compared to those obtained using frequency response analysis of the flight data. Time delays and damping ratios are compared and are in agreement. This technique demonstrates the potential to determine, in near real time, the extent of differences between computer models and the actual aircraft. Precise knowledge of these differences can help to determine the flying qualities of a test aircraft and lead to more efficient envelope expansion

A vehicle health monitoring system for the space shuttle reaction control system during reentry

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1995.Includes bibliographical references (p. 121-123).by Anthony David Rosello.M.S

Extended Kalman filter for integrating tracking data from ground-based radar and airborne global positioning system

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (leaf 89).by Mark P. Green.M.S

Development of a Prognostic Method for the Production of Undeclared Enriched Uranium

As global demand for nuclear energy and threats to nuclear security increase, the need for verification of the peaceful application of nuclear materials and technology also rises. In accordance with the Nuclear Nonproliferation Treaty, the International Atomic Energy Agency is tasked with verification of the declared enrichment activities of member states. Due to the increased cost of inspection and verification of a globally growing nuclear energy industry, remote process monitoring has been proposed as part of a next-generation, information-driven safeguards program. To further enhance this safeguards approach, it is proposed that process monitoring data may be used to not only verify the past but to anticipate the future via prognostic analysis. While prognostic methods exist for health monitoring of physical processes, the literature is absent of methods to predict the outcome of decision-based events, such as the production of undeclared enriched uranium. This dissertation introduces a method to predict the time at which a significant quantity of unaccounted material is expected to be diverted during an enrichment process. This method utilizes a particle filter to model the data and provide a Type III (degradation-based) prognostic estimate of time to diversion of a significant quantity. Measurement noise for the particle filter is estimated using historical data and may be updated with Bayesian estimates from the analyzed data. Dynamic noise estimates are updated based on observed changes in process data. The reliability of the prognostic model for a given range of data is validated via information complexity scores and goodness of fit statistics. The developed prognostic method is tested using data produced from the Oak Ridge Mock Feed and Withdrawal Facility, a 1:100 scale test platform for developing gas centrifuge remote monitoring techniques. Four case studies are considered: no diversion, slow diversion, fast diversion, and intermittent diversion. All intervals of diversion and non-diversion were correctly identified and significant quantity diversion time was accurately estimated. A diversion of 0.8 kg over 85 minutes was detected after 10 minutes and predicted to be 84 minutes and 10 seconds after 46 minutes and 40 seconds with an uncertainty of 2 minutes and 52 seconds

Single mode excitation in the shallow water acoustic channel using feedback control

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 1996The shallow water acoustic channel supports far-field propagation in a discrete set of modes. Ocean experiments have confirmed the modal nature of acoustic propagation, but no experiment has successfully excited only one of the suite of mid-frequency propagating modes propagating in a coastal environment. The ability to excite a single mode would be a powerful tool for investigating shallow water ocean processes. A feedback control algorithm incorporating elements of adaptive estimation, underwater acoustics, array processing and control theory to generate a high-fidelity single mode is presented. This approach also yields a cohesive framework for evaluating the feasibility of generating a single mode with given array geometries, noise characteristics and source power limitations. Simulations and laboratory waveguide experiments indicate the proposed algorithm holds promise for ocean experiments.Josko Catipovic funded my research for summer of 1992 on the Office of Naval Research Grant Number N00014-92-J-1661 and from June 1993 through August 1995 on Defense Advanced Research Projects Agency Grant Number MDA972-92-J- 1041. The Office of Naval Research Grant N00014-95-1-0362 to MIT supported the computer facilities used to do much of this work

Practical Implementation of Multiple Model Adaptive Estimation Using Neyman-Pearson Based Hypothesis Testing and Spectral Estimation Tools

This study investigates and develops various modifications to the Multiple Model Adaptive Estimation (MMAE) algorithm. The standard MMAE uses a bank of Kalman filters, each based on a different model of the system. Each of the filters predict the system response, based on its system model, to a given input and form the residual difference between the prediction and sensor measurements of the system response. Model differences in the input matrix, output matrix, and state transition matrix, which respectively correspond to an actuator failure, sensor failure, and an incorrectly modeled flight condition for a flight control failure application, were investigated in this research. An alternative filter bank structure is developed that uses a linear transform on the residual from a single Kalman filter to produce the equivalent residuals of the other Kalman filters in the standard MMAE. A Neyman Pearson based hypothesis testing algorithm is developed that results in significant improvement in failure detection performance when compared to the standard hypothesis testing algorithm. Hypothesis testing using spectral estimation techniques is also developed which provides superior failure identification performance at extremely small input levels