A nonlinear complementary filter approach for MAV 3D-attitude estimation with low-cost MARG/ADS

Attitude is an important parameter for micro-unmanned aerial vehicles (MAVs) during its autonomous and stable flying. The MAVs with small size required low power usually use low-cost magnetometer, accelerometer and gyroscope (MARG) to calculate attitude information. But the attitude errors always increase with time due to the gyroscope output corrupted by additive high noise levels and uncertain bias drift. Therefore, a robust and novel nonlinear complementary filter (NCF) approach for MAVs attitude estimation based on low-cost MARG and embedded air data sensors (ADS) is proposed to correct the integrated error and compensate the gyroscope bias on-line. Based on the MAVs kinematic equations and derived coordinate transformation matrix together with different measurement properties, an attitude observer as input is derived for NCF. Finally, a series of experiments are executed on a MAV platform called BH-1 to demonstrate the improved performances of proposed nonlinear complementary filter compared to traditional complementary filter (CF).