Keywords and Phrases
Estimation; Extended Kalman Filter; Inertial Navigation; Uncertainty
"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, an efficient and consistent propagation of state uncertainty with the multiplicative extended Kalman filter is achieved. Asimulation environment is developed to investigate the performance of the algorithms within a descent-to-landing scenario. Monte Carlo analysis is used to analyze the effects of the developed error propagation and the accompanying algorithms to compare them with commonly used discrete dead-reckoning approaches"--Abstract, page iii.
DeMars, Kyle J.
Pernicka, Henry J.
Mechanical and Aerospace Engineering
M.S. in Aerospace Engineering
Missouri University of Science and Technology
xiv, 124 pages
© 2019 James Daniel Alan Brouk, All rights reserved.
Thesis - Open Access
Electronic OCLC #
Brouk, James Daniel Alan, "Propagation of uncertainty through coning, sculling, and scrolling corrections for inertial navigation" (2019). Masters Theses. 7912.