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.