基于自适应UKF的卫星星座自主导航方法

Autonomous navigation method of satellite constellation based on adaptive UKF

针对卫星星座自主导航系统中存在的模型不确定性及难以准确获取的时变系统噪声统计特性影响导航精度的问题,提出了一种系统噪声在线自适应调整的UKF算法。基于所提出的自适应UKF算法设计了一种基于星间相对测量的卫星星座自主导航方法,该方法结合奇异值分解和比例修正的采样策略,解决了应用UKF时易出现状态误差方差阵丧失正定性而导致的Cholesky分解无法进行的问题。通过在低轨区域星座和中轨全球星座上的仿真实验,验证了该算法在提高滤波精度以及改善状态估计置信度方面的有效性,所提算法的定轨精度优于EKF算法、自适应EKF算法以及基于对称采样策略的UKF算法。采用CRLB分析法对导航算法的估计性能进行了分析验证。

The autonomous satellite constellation navigation system faces model uncertainty and is difficult to accurately obtain statistical characteristics of the time-varying system noise,thus affecting the navigation accuracy.To address this issue,an unscented Kalman filter(UKF)algorithm based on the online adaptive adjustment of system noise was proposed.An autonomous satellite constellation navigation method based on the relative measurement between satellites was designed according to the proposed adaptive UKF algorithm.This method combined the sampling strategy of singular value decomposition and scale correction to solve the problem that Cholesky decomposition cannot be carried out due to the loss of positive definiteness of the state error variance matrix when UKF was applied.Through the simulation results on a low earth orbit(LEO)local constellation and a middle earth orbit(MEO)global constellation,the effectiveness of the algorithm in improving the filtering accuracy and the confidence of state estimation was verified.Its orbit determination accuracy was better than the extended Kalman filter(EKF)algorithm,adaptive EKF algorithm,and UKF algorithm based on symmetrical sampling strategies.Finally,the Cramer-Rao lower bounds(CRLB)analysis method was used to verify the estimation performance of the algorithm.