基于自适应 UKF 的卫星星座自主导航方法

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.