Abstract: A parallel extended Kalman filter (PEKF) algorithm for autonomous navigation of satellite constellation with multiple earth satellites and one lunar satellite is proposed. By solving the measurement selection problem with noise statistic uncertainty, the appropriate measurement is selected to reduce the exterior disturbance. By solving the measurement scheduling problem in the case of noise statistics uncertainty, the appropriate measurement is selected to reduce the impact of interferenceIn order to adaptively select appropriate measurements, the PEKF based on multiple subsets of measurements from different sources is proposed. Each extended Kalman filter (EKF) is used to process different measurement subsets, and the weight of the sub filters is calculated based on the residual sequence, compared with EKF and traditional multi model adaptive estimation (MMAE) algorithm, the results show that the proposed method is stable in the threeaxis position estimation error under the condition of interference, which reflects the advantage of the performance.

Key words: parallel extended Kalman filters, autonomous navigation, noise statistic uncertainty；satellite constellation, measurement selection