Fuzzy adaptive Kalman filter algorithm for RUAV´s integrated navigation system

The Kalman filter has characteristics of the noise-sensitive. This paper analyzes the adaptive Kalman filter algorithms which are based on Sage-Husae, neural network and fuzzy logic method. And an adaptive Kalman filter based on fuzzy logic is designed to estimate the attitude, heading and velocity of the RUAV. Combining the characteristics of RUAV platform and analyzing the real flight data, the fuzzy inference rules are designed to change the filtering parameters. With the actual flight data, the simulation verifies the validity of this algorithm. The experiments prove that this method can improve the navigation precision of RUAV.