Attitude estimation using MARG sensors for unmanned aerial vehicles

Abstract

In this paper we discuss an extended kalman filter with novel magnetic vector measurement model for attitude estimation of unmanned aerial vehicles (UAVs). As MARG (Magnetic, Angular Rate and Gravity) sensors are used, this extended kalman filter selects attitude error and angular rate drift as the filter states, and performs the information fusion using the magnetic and gravity vector respectively measured by the three-axis accelerometer and the three-axis magnetometer. We propose a new method of magnetic vector fusion different from the past ones. Firstly, the measured earth magnetic vector is transformed from body frame to local frame using estimate direction cosine matrix. And then the innovation is carefully constructed using the cross product between measured vector and referenced earth magnetic vector, which is different from the past vector subtract way. Finally, the measurement model of magnetic vector and equations of kalman filter is presented. Proposed algorithm is implemented on the Cortex-M4 microprocessor platform and experiments is taken relied on three-axis turntable. The results show that the proposed method has a lower computational cost than the past methods, and is able to track the attitude of motions precisely.