Robust Adaptive Filter for Time-Varying Parameters Estimation in Integrated Navigation of Fixed-Wing Aerial Robot*

Abstract

This paper focuses on the autonomous navigation problem of the fixed-wing aerial robot, of which the inertial measurement unit (IMU) has time-varying error characteristic parameters. A robust variational Bayesian adaptive Kalman filter (RVBAKF) is proposed to tackle the noise outliers in integrated navigation brought by sensor uncertainties. The proposed method formulates the joint probability distribution of the system state vector and the measurement noise covariance matrix. A fading factor matrix with strong tracking quality is introduced to enhance the prediction on the prior distribution of the process noise covariance. Then a weighted sliding window mechanism has been constructed to obtain the posterior distribution of the measurement noise outliers. Therefore, the proposed approach is impressive in approximating both the process and measurement noise. The RVBAKF algorithm is implemented in an integrated navigation system which is composed of the strapdown inertial navigation system (SINS) and the global navigation satellite system (GNSS). The integration framework based on RVBAKF is conducted on two typical verification scenarios, which is proven to be exceptional in coping with the time-varying process and measurement noise by comparing the average root mean square error in misalignment angle, velocity and position with the strong tracking filter and the variational Bayesian filter.