Robust Attitude Estimation Using Magnetic and Inertial Sensors

Lightweight and low-cost magnetic and inertial sensors are commonly used for attitude estimation in a wide range of applications, from motion tracking to autonomous navigation. However, the inherent and external sensor errors and the accelerations due to the actual motion of the platform highly affect the estimation accuracy. This study proposes a robust attitude estimation algorithm that compensates the sensor errors and the external accelerations at the algorithm level. The attitude estimation filter, which is structured as a Multiplicative Extended Kalman Filter (MEKF), is modified to compensate for both the long-term and short-term measurement uncertainties. Modification is applied mainly by estimating and compensating the bias and external accelerations for long-term uncertainties and tuning the measurement noise covariance matrix for short-term uncertainties. Simulations test the proposed algorithm, and the results are compared with benchmark algorithms.