A linear fusion algorithm for attitude determination using low cost MEMS-based sensors

This paper presents a novel sensing methodology with an extended Kalman-based fusion algorithm for attitude estimation, using inexpensive micromachined gyroscopes, accelerometers and magnetometers. Unlike conventional methodology using quaternions and Euler angles, in the proposed fusion algorithm the state vector is defined to be a 6 × 1 vector containing sensing components of earth gravity and magnetic field in the body frame. By this way, the Kalman model can be represented by linear equations, which makes the iterative computations easy to be implemented at a faster rate using inexpensive microprocessors. The computation of the filter is further simplified by updating gravity and magnetic vectors respectively in smaller dimension. Experiments are performed to validate the effectiveness of the proposed approach.