Generalized complementary filter for attitude estimation based on vector observations and cross products

A generalized complementary filter (GCF) for attitude estimation is presented in this paper, which is based on vector observation and its cross product. With brief reviews of introductions and discussions of complementary filters in the existing literature, it is pointed out that the vector cross product plays a key role in the basis of complementary attitude filter. Both the estimation and compensation of attitude error is carried out by means of cross products. Numerical simulation and application test are performed to evaluate the proposed GCF. Simulation and experiment results show that the proposed GCF has better numerical stability and much higher computational efficiency than the multiplicative extended Kalman filter (MEKF).