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Full-Text Articles in Engineering
Propagation Of Uncertainty Through Coning, Sculling, And Scrolling Corrections For Inertial Navigation, James Daniel Alan Brouk
Propagation Of Uncertainty Through Coning, Sculling, And Scrolling Corrections For Inertial Navigation, James Daniel Alan Brouk
Masters Theses
"This thesis investigates the propagation of estimation errors through generalized coning, sculling, and scrolling algorithms used in modern day inertial navigation systems, in order to accurately quantify the uncertainty in the estimation of position, velocity, and attitude. The corrections for coning, sculling, and scrolling algorithms have an often unaccounted for effect on documented and empirically derived error statistics for measurements used to predict the uncertainty in a vehicle's position, velocity, and attitude estimates. Through the development of an error analysis for these generalized algorithms, mappings of the measurement and estimation errors through the correction termare generated. Using the developed mappings, …
Multitarget Tracking And Terrain-Aided Navigation Using Square-Root Consider Filters, James Samuel Mccabe
Multitarget Tracking And Terrain-Aided Navigation Using Square-Root Consider Filters, James Samuel Mccabe
Doctoral Dissertations
"Filtering is a term used to describe methods that estimate the values of partially observed states, such as the position, velocity, and attitude of a vehicle, using current observations that are corrupted due to various sources, such as measurement noise, transmission dropouts, and spurious information. The study of filtering has been an active focus of research for decades, and the resulting filters have been the cornerstone of many of humankind's greatest technological achievements. However, these achievements are enabled principally by the use of specialized techniques that seek to, in some way, combat the negative impacts that processor roundoff and truncation …
Discrete-Time Neural Network Based State Observer With Neural Network Based Control Formulation For A Class Of Systems With Unmatched Uncertainties, Jason Michael Stumfoll
Discrete-Time Neural Network Based State Observer With Neural Network Based Control Formulation For A Class Of Systems With Unmatched Uncertainties, Jason Michael Stumfoll
Masters Theses
"An observer is a dynamic system that estimates the state variables of another system using noisy measurements, either to estimate unmeasurable states, or to improve the accuracy of the state measurements. The Modified State Observer (MSO) is a technique that uses a standard observer structure modified to include a neural network to estimate system states as well as system uncertainty. It has been used in orbit uncertainty estimation and atmospheric reentry uncertainty estimation problems to correctly estimate unmodeled system dynamics. A form of the MSO has been used to control a nonlinear electrohydraulic system with parameter uncertainty using a simplified …