Fractional-order complementary filters for small unmanned aerial system attitude estimation

For small unmanned aerial systems (sUAS), the problem of estimation of orientation angles relative to the Earths surface (attitude) is of major importance. Usually, this estimation is performed by the combination of several kinds of sensor data by a Kalman filter or complementary-style filter producing a navigation-quality solution. Kalman filters make assumptions about linearity and statistical properties of the sensor noise, which must be accurate for proper functioning of the filter. Another option, the complementary filter, is more simple, and does not make these assumptions. Although the applications of the complementary filter have been researched in depth, the complementary filter can be combined in a novel way with fractional-order filters for the purpose of small UAS flight. In this paper, a roll-pitch fractional-order estimator is shown in flight configuration (real-time code) on a small sUAS. With fewer tunable parameters, we show that the fractional-order filter is more simple to implement. Realtime results are shown which indicate adequate performance relative to an existing quaternion-based Kalman filter-based estimator.