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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 &#x210B;<inf>2</inf> estimation called extended &#x210B;<inf>2</inf> 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 &#x210B;<inf>2</inf> optimal estimator as the extended &#x210B;<inf>2</inf> 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.

Nonlinear Attitude Estimation for Small UAVs with Low Power Microprocessors