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

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

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

17 Scopus citations

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 - 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
Country/TerritoryUnited States
CityPortland, OR
Period8/8/118/11/11

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Modeling and Simulation

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