Design of orientation estimation system by inertial and magnetic sensors

Abstract

The purpose of this paper is to present orientation estimation for small unmanned aerial vehicle (SUAV). An extended Kalman filter with adaptive PR (P denotes the estimation error covariance matrix, and R denotes the measurement noise covariance matrix) is designed to estimate orientation by sensors of gyroscope, accelerometer, and magnetometer integrated in Micro Electronic Mechanic System-based heading reference systems. Since ferromagnetic materials or other magnetic fields near the magnetometer disturb the measurement of local earth magnetic field and the external forces which produce maneuvering acceleration effect the measurement of gravity by the accelerometer, the orientation estimation is disturbed. Accordingly, the error equations of sensors are established using a current statistical model, and then the extended Kalman filter with adaptive PR with 12 state variables is designed. In the filter, the orientation error, gyroscope offset error, magnetic disturbance error, and maneuvering acceleration error are estimated. The swing experiment in hand with the magnetic disturbance and small maneuvering acceleration, and flight experiment for SUAV with the magnetic disturbance and large maneuvering acceleration, are developed. The compensation results show that the orientation is accurately calculated with disturbances. A new methodology for the orientation estimation is proposed, which could also be considered for other special application such as the robot on the ground and the autonomous underwater vehicles. This paper provides a novel realization method for accurate orientation estimation for SUAV. The method can be applied in many applications with a simple hardware.