  Among algorithms used for sensor fusion for attitude estimation in unmanned
aerial vehicles, the Extended Kalman Filter (EKF) is the most commonly used for
estimation. In this paper, we propose a new version of H2 estimation called
extended H2 estimation that can overcome the limitations of the extended Kalman
Filter, specifically with respect to computational speed, memory usage, and
root mean squared error. We formulate a new attitude-estimation algorithm,
where the filter gain is designed offline about a nominal operating point, but
the filter dynamics is implemented using the nonlinear system dynamics. We
refer to this implementation of the H2 optimal estimator as the extended H2
estimator. The solution presented is tested on two cases, corresponding to slow
and rapid motions, and compared against the EKF in the performance metrics
mentioned above.
