A Study of the Effects of Stochastic Inertial Sensor Errors in Dead-Reckoning Navigation. Wall, J. August 2007. Accepted: 2008-09-09T21:25:39Z![A Study of the Effects of Stochastic Inertial Sensor Errors in Dead-Reckoning Navigation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex The research presented in this thesis seeks to quantify the error growth of navigation frame attitude, velocity, and position as solely derived from acceleration and rotation- rate measurements from a strapdown Inertial Measurement Unit (IMU). The wide-spread availability of the Global Positioning System (GPS) and increased technological advances in Inertial Navigation Systems (INS) technology has made possible the use of increasingly affordable and compact GPS/INS navigation systems. While the fusion of GPS and inertial sensing technology offers exceptional performance under nominal conditions, the accuracy of the provided solution degrades rapidly when traveling under bridges, dense foliage, or in urban canyons due to loss of communication with GPS satellites. The degradation of the navigation solution in this inertial dead-reckoning mode is a direct result of the numerical integration of stochastic errors exhibited by the inertial sensors themselves. As the accuracy of the GPS/INS combined system depends heavily on the standalone performance of the INS, firm quantification of the performance of inertial dead-reckoning is imperative for system selection and design. To provide quantification of the accuracy of inertial dead-reckoning, stochastic mod- els are selected which approximate the noise and bias drift present on a wide variety of both accelerometers and rate-gyroscopes. The stochastic identification techniques of Al- lan variance and experimental autocorrelation are presented to illustrate the extraction of process parameters from experimental data using the assumed model forms. The selected models are then used to develop analytical expressions for the variance of subse- quent integrations of the stochastic error processes. The resulting analytical expressions are validated using Monte Carlo simulations. The analytical analysis is extended to a simple navigation scenario in which a vehicle is constrained to travel on a planar surface with no lateral velocity. Monte Carlo simulation techniques are employed to exemplify and compare the expected results of inertial navigation in higher dynamic scenarios.
@unpublished{wall_study_2007,
type = {Thesis},
title = {A {Study} of the {Effects} of {Stochastic} {Inertial} {Sensor} {Errors} in {Dead}-{Reckoning} {Navigation}},
url = {https://etd.auburn.edu//handle/10415/945},
abstract = {The research presented in this thesis seeks to quantify the error growth of navigation
frame attitude, velocity, and position as solely derived from acceleration and rotation-
rate measurements from a strapdown Inertial Measurement Unit (IMU). The wide-spread
availability of the Global Positioning System (GPS) and increased technological advances
in Inertial Navigation Systems (INS) technology has made possible the use of increasingly
affordable and compact GPS/INS navigation systems. While the fusion of GPS and
inertial sensing technology offers exceptional performance under nominal conditions, the
accuracy of the provided solution degrades rapidly when traveling under bridges, dense
foliage, or in urban canyons due to loss of communication with GPS satellites. The
degradation of the navigation solution in this inertial dead-reckoning mode is a direct
result of the numerical integration of stochastic errors exhibited by the inertial sensors
themselves. As the accuracy of the GPS/INS combined system depends heavily on the
standalone performance of the INS, firm quantification of the performance of inertial
dead-reckoning is imperative for system selection and design.
To provide quantification of the accuracy of inertial dead-reckoning, stochastic mod-
els are selected which approximate the noise and bias drift present on a wide variety of
both accelerometers and rate-gyroscopes. The stochastic identification techniques of Al-
lan variance and experimental autocorrelation are presented to illustrate the extraction
of process parameters from experimental data using the assumed model forms. The
selected models are then used to develop analytical expressions for the variance of subse-
quent integrations of the stochastic error processes. The resulting analytical expressions
are validated using Monte Carlo simulations. The analytical analysis is extended to a
simple navigation scenario in which a vehicle is constrained to travel on a planar surface
with no lateral velocity. Monte Carlo simulation techniques are employed to exemplify
and compare the expected results of inertial navigation in higher dynamic scenarios.},
language = {en\_US},
urldate = {2024-06-25},
author = {Wall, John},
month = aug,
year = {2007},
note = {Accepted: 2008-09-09T21:25:39Z},
}
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While the fusion of GPS and inertial sensing technology offers exceptional performance under nominal conditions, the accuracy of the provided solution degrades rapidly when traveling under bridges, dense foliage, or in urban canyons due to loss of communication with GPS satellites. The degradation of the navigation solution in this inertial dead-reckoning mode is a direct result of the numerical integration of stochastic errors exhibited by the inertial sensors themselves. As the accuracy of the GPS/INS combined system depends heavily on the standalone performance of the INS, firm quantification of the performance of inertial dead-reckoning is imperative for system selection and design. To provide quantification of the accuracy of inertial dead-reckoning, stochastic mod- els are selected which approximate the noise and bias drift present on a wide variety of both accelerometers and rate-gyroscopes. The stochastic identification techniques of Al- lan variance and experimental autocorrelation are presented to illustrate the extraction of process parameters from experimental data using the assumed model forms. The selected models are then used to develop analytical expressions for the variance of subse- quent integrations of the stochastic error processes. The resulting analytical expressions are validated using Monte Carlo simulations. The analytical analysis is extended to a simple navigation scenario in which a vehicle is constrained to travel on a planar surface with no lateral velocity. Monte Carlo simulation techniques are employed to exemplify and compare the expected results of inertial navigation in higher dynamic scenarios.","language":"en_US","urldate":"2024-06-25","author":[{"propositions":[],"lastnames":["Wall"],"firstnames":["John"],"suffixes":[]}],"month":"August","year":"2007","note":"Accepted: 2008-09-09T21:25:39Z","bibtex":"@unpublished{wall_study_2007,\n\ttype = {Thesis},\n\ttitle = {A {Study} of the {Effects} of {Stochastic} {Inertial} {Sensor} {Errors} in {Dead}-{Reckoning} {Navigation}},\n\turl = {https://etd.auburn.edu//handle/10415/945},\n\tabstract = {The research presented in this thesis seeks to quantify the error growth of navigation\nframe attitude, velocity, and position as solely derived from acceleration and rotation-\nrate measurements from a strapdown Inertial Measurement Unit (IMU). The wide-spread\navailability of the Global Positioning System (GPS) and increased technological advances\nin Inertial Navigation Systems (INS) technology has made possible the use of increasingly\naffordable and compact GPS/INS navigation systems. While the fusion of GPS and\ninertial sensing technology offers exceptional performance under nominal conditions, the\naccuracy of the provided solution degrades rapidly when traveling under bridges, dense\nfoliage, or in urban canyons due to loss of communication with GPS satellites. The\ndegradation of the navigation solution in this inertial dead-reckoning mode is a direct\nresult of the numerical integration of stochastic errors exhibited by the inertial sensors\nthemselves. As the accuracy of the GPS/INS combined system depends heavily on the\nstandalone performance of the INS, firm quantification of the performance of inertial\ndead-reckoning is imperative for system selection and design.\n\nTo provide quantification of the accuracy of inertial dead-reckoning, stochastic mod-\nels are selected which approximate the noise and bias drift present on a wide variety of\nboth accelerometers and rate-gyroscopes. The stochastic identification techniques of Al-\nlan variance and experimental autocorrelation are presented to illustrate the extraction\nof process parameters from experimental data using the assumed model forms. The\nselected models are then used to develop analytical expressions for the variance of subse-\nquent integrations of the stochastic error processes. The resulting analytical expressions\nare validated using Monte Carlo simulations. The analytical analysis is extended to a\nsimple navigation scenario in which a vehicle is constrained to travel on a planar surface\nwith no lateral velocity. 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