TY - GEN
T1 - On-line modeling and calibration of low-cost navigation sensors
AU - Gross, Jason N.
AU - Gu, Yu
AU - Rhudy, Matthew
AU - Barchesky, Francis J.
AU - Napolitano, Marcello R.
N1 - Funding Information:
The authors would like to thank Michael Gribskov for providing a carefully curated 4Fe-4S ferredoxin data set. and the reviewers for useful comments and suggestions. William Grundy is funded by the National Defense Science and Engineering Grant Fellowship Program. Charles Elkan is funded by a Hellman Faculty Fellowship from UCSD. Paragon time was made available through a grant to NBCR (NIH P4I RR08605). Timothy L. Bailey was supported by the National Biomedical Computation Resource, an NIH/NCRR funded research resource (P41 RR-08605). and the NSF through cooperative agreement ASC-02827.
PY - 2011
Y1 - 2011
N2 - In this paper, calibration modeling of a low-cost Inertial Measurement Unit (IMU) sensor for Small Unmanned Aerial Vehicle (SUAV) attitude estimation is considered. First, an Allan variance analysis method is used to determine stochastic noise model parameters for each sensor of a Micro-Electro-Mechanical-System (MEMS) IMU. Next, these models are included in a Global Positioning System/Inertial Navigation System (GPS/INS) sensor fusion algorithm for on-line calibration. In addition, an off-line magnetometer calibration is considered that uses a set of GPS/INS sensor fusion attitude estimates to derive a calibration model. This off-line magnetometer calibration model is then augmented on-line with sensor fusion estimates of the residual sensor biases. Finally, using the calibrated magnetometers, attitude estimation is considered that uses only a low-cost IMU with magnetometers. Each sensor fusion algorithm is formulated using an Unscented Kalman Filter (UKF). For performance validation, attitude estimates are calculated with data collected on-board a SUAV and are compared with high-quality vertical gyroscope measurements.
AB - In this paper, calibration modeling of a low-cost Inertial Measurement Unit (IMU) sensor for Small Unmanned Aerial Vehicle (SUAV) attitude estimation is considered. First, an Allan variance analysis method is used to determine stochastic noise model parameters for each sensor of a Micro-Electro-Mechanical-System (MEMS) IMU. Next, these models are included in a Global Positioning System/Inertial Navigation System (GPS/INS) sensor fusion algorithm for on-line calibration. In addition, an off-line magnetometer calibration is considered that uses a set of GPS/INS sensor fusion attitude estimates to derive a calibration model. This off-line magnetometer calibration model is then augmented on-line with sensor fusion estimates of the residual sensor biases. Finally, using the calibrated magnetometers, attitude estimation is considered that uses only a low-cost IMU with magnetometers. Each sensor fusion algorithm is formulated using an Unscented Kalman Filter (UKF). For performance validation, attitude estimates are calculated with data collected on-board a SUAV and are compared with high-quality vertical gyroscope measurements.
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M3 - Conference contribution
AN - SCOPUS:84876025454
SN - 9781624101540
T3 - AIAA Modeling and Simulation Technologies Conference 2011
SP - 298
EP - 311
BT - AIAA Modeling and Simulation Technologies Conference 2011
T2 - AIAA Modeling and Simulation Technologies Conference 2011
Y2 - 8 August 2011 through 11 August 2011
ER -