针对容积卡尔曼滤波(CKF)在捷联惯导系统(SINS)初始对准时由于模型误差和外界扰动导致滤波精度下降和鲁棒性差的问题,提出一种简化多重渐消因子强跟踪CKF算法(RMSTCKF),并给出了算法流程和推导了多重渐消因子的次优解法。多重渐消因子可以根据不同状态的不确定性程度大小相应提高各个状态的跟踪能力,具有较强的自适应性和鲁棒性,将RMSTCKF应用于由欧拉平台误差角(EPEA)描述的大方位失准角误差方程,在系统噪声不匹配和基座受扰动两种情况下进行仿真,并与RSTCKF、RCKF两种算法进行对比实验,结果表明,RMSTCKF的滤波精度和收敛速度明显优于RSTCKF和RCKF,具有更高的工程实用价值。

Considering that the cubature Kalman filter (CKF) has low accuracy and poor robustness in the application of SINS initial alignment, a ruduced multiple fading factors strong tracking CKF (RMSTCKF) is presented, the algorithm scheme is given and the suboptimal solution of Multiple fading factors (MFF) is derived. MFF can improve tracking capability in each state according to the uncertainty in different states, which has stronger adaptivity and robustness. RMSTCKF is applied in large azimuth misalignment angle error equation, which is described by EPEA, the simulation is carried out under two condition, including the state of mismatching system noise and the state of disturbed base. Compared with RSTCKF and RCKF, the simulation results of RMSTCKF show that RMSTCKF is obviously superior to RSTCKF and RCKF in filtering precision and rate of convergence, and has stronger practical values.