基于双滤波器的高精度目标跟踪估计方法

针对连续推力的合作航天器,采用双重无迹卡尔曼滤波(DUKF)算法估计其状态和加速度.通过状态滤波器和参数滤波器的配合,提升滤波精度,完成运动状态和参数的估计,从而实现合作目标的运动轨迹跟踪.与合作航天器相比,非合作航天器存在大小未知、发生时刻未知的机动,无法获得加速度,且信息获取和运动状态的估计难度大.针对非合作航天器...

High-accuracy target tracking estimation based on dual Kalman filter

For cooperative spacecraft driven by continuous force, the dual unscented Kalman filter (DUKF) was used to estimate the state and acceleration. The state filter and parameter filter can cooperate mutually to ensure higher filter accuracy of estimation for motions and parameters, so that the motion tracking of cooperative targets can be realized. Compared with cooperative targets, non-cooperative targets perform maneuvering with unknown forces and occurrence time, so this brings more challenges for information acquisition and state estimation. For non-cooperative spacecraft, we formulated the relative motion equation, and utilized the observation information from space-based platform for state estimation. Two extended Kalman filters (EKFs) and semi latus rectum were used for maneuvering detection to estimate the motion state of maneuverings with multiple unknown pulses. The simulation results show that DUKF has faster rate of convergence and better performance of tracking error for state and acceleration estimation of cooperative spacecraft. For non-cooperative spacecraft, the effectiveness of the maneuvering detection strategy combining with the filter switching strategy is verified through comparison. The proposed method detected multiple maneuvers and reduced misjudgments