Deterministic and stochastic error modeling of inertial sensors and magnetometers
Author(s)
Advisor
Barshan, BillurDate
2012Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
This thesis focuses on the deterministic and stochastic modeling and model parameter
estimation of two commonly employed inertial measurement units. Each
unit comprises a tri-axial accelerometer, a tri-axial gyroscope, and a tri-axial
magnetometer. In the first part of the thesis, deterministic modeling and calibration
of the units are performed, based on real test data acquired from a flight
motion simulator. The deterministic modeling and identification of accelerometers
is performed based on a traditional model. A novel technique is proposed for
the deterministic modeling of the gyroscopes, relaxing the test bed requirement
and enabling their in-use calibration. This is followed by the presentation of a
new sensor measurement model for magnetometers that improves the calibration
error by modeling the orientation-dependent magnetic disturbances in a gimbaled
angular position control machine. Model-based Levenberg-Marquardt and modelfree
evolutionary optimization algorithms are adopted to estimate the calibration
parameters of sensors. In the second part of the thesis, stochastic error modeling
of the two inertial sensor units is addressed. Maximum likelihood estimation
is employed for estimating the parameters of the different noise components of
the sensors, after the dominant noise components are identified. Evolutionary
and gradient-based optimization algorithms are implemented to maximize the
likelihood function, namely particle swarm optimization and gradient-ascent optimization.
The performance of the proposed algorithm is verified through experiments
and the results are compared to the classical Allan variance technique.
The results obtained with the proposed approach have higher accuracy and require
a smaller sample data size, resulting in calibration experiments of shorter
duration. Finally, the two sensor units are compared in terms of repeatability,
present measurement noise, and unaided navigation performance.
Keywords
Inertial sensorsdeterministic error modeling
stochastic error modeling
in-field calibration
Levenberg-Marquardt algorithm
particle swarm optimization
gradient-ascent optimization
Allan variance
maximum likelihood estimation