Abstract: Abstract： The successful use of the standard extended Kalman filter (EKF) is restricted by the requirement on the statistics information of the measurement noise. The filtering performance may decline due to the statistical uncertainty. Although the adaptive extended Kalman filter (AEKF) is available for recursive covariance estimation, it is often less accurate than the EKF with accurate noise statistics. Aiming at this problem, this paper develops a parallel adaptive extended Kalman filter (PAEKF) by combining the EKF and the AEKF with an adaptive law, such that the final state estimate is dominated by the EKF when the prior noise covariance is accurate, while the AEKF is activated when the actual noise covariance deviates from its nominal value. The PAEKF can reduce the sensitivity of the algorithm to the model uncertainty, and ensure the estimation accuracy in the normal case. For spacecraft relative attitude and position estimation, the simulation results demonstrate that the PAEKF has the advantage of both the AEKF and the EKF.

Key words: Keywords： adaptive extended Kalman filter, recursive covariance estimation, space target, relative attitude and position, state estimation