Orientation estimation is very important for development of unmanned aerial systems (UASs), and is performed by combining data from several sources and sensors. Kalman filters are widely used for this task, however they typically assume linearity and Gaussian noise statistics. While these assumptions work well for high-quality, high-cost sensors, it does not work as well for low-cost, low-quality sensors. For low-cost sensors, complementary filters can be used since no assumptions are made with regards to linearity and noise statistics. In this article, the history and basics of complementary filters are included with examples, the concepts of filtering based on fractional-order calculus are applied to the complementary filter, and the efficacy of non-integer-order filtering on systems with non-Gaussian noise is explored with good success.