基于并行扩展卡尔曼滤波的自主卫星星座导航方法

针对星间距离测量容易受到外界干扰的问题，提出了一种适用于多颗地球卫星和一颗月球卫星的卫星星座自主导航的并行扩展卡尔曼滤波算法.通过解决噪声统计不确定情况下的测量调度问题来选择适当的测量，降低干扰的影响.为了自适应地选择适当的测量，提出一种基于不同来源的测量构造多个子集的并行扩展卡尔曼滤波器，其中每个扩展卡尔曼滤波器用于处理不同测量子集，并基于残差序列计算子滤波器的权重，组合并行滤波器的估计结果.通过与EKF和传统的多模型自适应估计算法进行比较，表明所提出方法在干扰条件下的三轴位置估计误差的稳定性，体现了性能优势.

Autonomous Satellite Constellation Navigation Method Based on Parallel Extended Kalman Filter

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.