Novel Adaptive Fuzzy Extended Kalman Filter for Attitude Estimation in Gps-Denied Environment

Abstract

This paper presents a Novel Adaptive Fuzzy Extended Kalman Filter namely (NAFEKF) which has been developed and applied for attitude estimation using only the outputs of strap-down IMU (Gyroscopes and Accelerometers) and strap-down magnetometer. The NAFEKF, which is based on EKF (Extended Kalman Filter) aided by FIS (Fuzzy Inference System), is validated in Matlab environment on simulated trip data and real data acquired during an UAV’s trip. Accuracy of estimated attitude is increased using NAFEKF compared to typical EKF and in addition the measurement noise covariance matrix is tuned, the proposed filter uses multiplicative error for quaternion. Simulation results show that estimated measurement noise covariance matrix is closed to its true value in cruise phase of flight (stationary phase), while in nonstationary phase it refers to the validity of accelerometer measurement model in the filter in NAFEKF; it neglects measurements from accelerometers in this case.