Iterative Noise Estimation-Based Cubature Kalman Filtering for Distributed POS in Aerial Earth Observation Imaging

Airborne distributed position and orientation system (DPOS) plays an important role in assisting the observation loads in aerial remote sensing imaging, where the imaging quality is decided by the accuracy of DPOS. Affected by the working environment, the measurement system of DPOS is prone to develop unknown measurement noise covariance, which will affect the accuracy of the transfer alignment of DPOS. To solve this problem, an adaptive cubature Kalman filter (ACKF) based on iterative measurement noise covariance is proposed in this paper, which uses the mean and variance of the innovation information to realize the recursive update of the measurement noise covariance. The experimental results based on flight data show that the estimation accuracy of ACKF at the aircraft maneuver period is higher than those of ACKF-VB (variational Bayesian) and CKF. Especially in the attitude, the estimation accuracy of ACKF is higher than CKF by more than 1 orders of magnitude. In the aircraft smooth flight period, ACKF converges faster than the ACKF-VB and CKF in the initial period of the heading and pitch angles. In summary, the experiment proves that the ACKF has a faster response speed, a reliable robustness and a strong ability to track measurement noise covariance.