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Initial Alignment of Large Azimuth Misalignment Angle in SINS based on Reduced Multiple Fading Factors Strong Tracking CKF

机译:基于减少的多衰落因子强大跟踪CKF循环中大方位角错位角度初始对准

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Aiming at the problem that Cubature Kalman Filter(CKF) has low accuracy and robustness under the condition of strapdown inertial navigation system(SINS) initial alignment due to model error and external disturbance, Reduced Multiple Strong Tracking Cubature Kalman Filter(RMSTCKF) is proposed, and the algorithm flow and sub-optimal solution of multiple fading factor are derived. Multiple fading factor can improve tracking ability under each state according to the degree of uncertainty of different states, having stronger adaptability and robustness. Applying RMSTCKF to large azimuth misalignment angle error function described by Euler platform error angle(EPEA), carrying out the simulation under two different conditions, namely noise mismatch and the base is disturbed, and making contrast between RSTCKF and RCKF, the simulation results show that the filter accuracy and convergence rate of RMSTCK when system noise mismatches with true noise are obviously better than RSTCKF and RCKF, having better practical value in engineering.
机译:针对建议数值积分卡尔曼滤波(CKF)具有捷联惯性导航系统(SINS)初始对准由于模型误差和外部扰动,出现低多强跟踪数值积分卡尔曼滤波的情况下低的精度和鲁棒性的问题(RMSTCKF),和多个衰减因子的算法流程和次优解导出。多个衰减因子可以根据不同的状态中的不确定性的程度每个状态下提高跟踪能力,具有更强的适应性和鲁棒性。施加RMSTCKF由欧拉平台误差角(EPEA)中描述的大方位错位角误差函数,两个不同的条件,即噪声失配和基部被扰乱,并且RSTCKF和RCKF之间进行对比下进行仿真,仿真结果显示当真正的噪音系统噪声的错配是RMSTCK的过滤精度和收敛速度明显优于RSTCKF和RCKF,具有较好的工程实用价值。

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