Orientation estimation is very important for development of unmanned aerial systems (UAS). Kalman filters are widely used; however they assume linearity and Gaussian statistics. While these assumptions work well for high-quality, high-cost sensors, they do not work as well for low-cost, low-quality sensors. In these cases, complementary filters are used since no assumptions are made with regards to linearity and statistics. This paper gives a review of the different types of complementary filters being developed. Basic examples are shown with details about how they are derived and simulations that show how they work. Guidelines are then given about when complementary filters are best used for UAS navigation.