On-line modeling and calibration of low-cost navigation sensors

Jason N. Gross, Yu Gu, Matthew Brandon Rhudy, Francis J. Barchesky, Marcello R. Napolitano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Title of host publicationAIAA Modeling and Simulation Technologies Conference 2011
Pages298-311
Number of pages14
StatePublished - Dec 1 2011
EventAIAA Modeling and Simulation Technologies Conference 2011 - Portland, OR, United States
Duration: Aug 8 2011Aug 11 2011

Publication series

NameAIAA Modeling and Simulation Technologies Conference 2011

Other

OtherAIAA Modeling and Simulation Technologies Conference 2011
CountryUnited States
CityPortland, OR
Period8/8/118/11/11

Fingerprint

Sensor Fusion
Navigation
Calibration
Inertial Navigation System
Sensor
Magnetometers
Global Positioning System
Model Calibration
Sensors
Units of measurement
Modeling
Fusion reactions
Unit
Costs
Estimate
Inertial navigation systems
Unmanned aerial vehicles (UAV)
Gyroscope
Line
Micro-electro-mechanical Systems

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Modeling and Simulation

Cite this

Gross, J. N., Gu, Y., Rhudy, M. B., Barchesky, F. J., & Napolitano, M. R. (2011). On-line modeling and calibration of low-cost navigation sensors. In AIAA Modeling and Simulation Technologies Conference 2011 (pp. 298-311). (AIAA Modeling and Simulation Technologies Conference 2011).
Gross, Jason N. ; Gu, Yu ; Rhudy, Matthew Brandon ; Barchesky, Francis J. ; Napolitano, Marcello R. / On-line modeling and calibration of low-cost navigation sensors. AIAA Modeling and Simulation Technologies Conference 2011. 2011. pp. 298-311 (AIAA Modeling and Simulation Technologies Conference 2011).
@inproceedings{123882a1b11a4f299582063f0e51056e,
title = "On-line modeling and calibration of low-cost navigation sensors",
abstract = "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.",
author = "Gross, {Jason N.} and Yu Gu and Rhudy, {Matthew Brandon} and Barchesky, {Francis J.} and Napolitano, {Marcello R.}",
year = "2011",
month = "12",
day = "1",
language = "English (US)",
isbn = "9781624101540",
series = "AIAA Modeling and Simulation Technologies Conference 2011",
pages = "298--311",
booktitle = "AIAA Modeling and Simulation Technologies Conference 2011",

}

Gross, JN, Gu, Y, Rhudy, MB, Barchesky, FJ & Napolitano, MR 2011, On-line modeling and calibration of low-cost navigation sensors. in AIAA Modeling and Simulation Technologies Conference 2011. AIAA Modeling and Simulation Technologies Conference 2011, pp. 298-311, AIAA Modeling and Simulation Technologies Conference 2011, Portland, OR, United States, 8/8/11.

On-line modeling and calibration of low-cost navigation sensors. / Gross, Jason N.; Gu, Yu; Rhudy, Matthew Brandon; Barchesky, Francis J.; Napolitano, Marcello R.

AIAA Modeling and Simulation Technologies Conference 2011. 2011. p. 298-311 (AIAA Modeling and Simulation Technologies Conference 2011).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - On-line modeling and calibration of low-cost navigation sensors

AU - Gross, Jason N.

AU - Gu, Yu

AU - Rhudy, Matthew Brandon

AU - Barchesky, Francis J.

AU - Napolitano, Marcello R.

PY - 2011/12/1

Y1 - 2011/12/1

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.

UR - http://www.scopus.com/inward/record.url?scp=84876025454&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84876025454&partnerID=8YFLogxK

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

ER -

Gross JN, Gu Y, Rhudy MB, Barchesky FJ, Napolitano MR. On-line modeling and calibration of low-cost navigation sensors. In AIAA Modeling and Simulation Technologies Conference 2011. 2011. p. 298-311. (AIAA Modeling and Simulation Technologies Conference 2011).