强跟踪CKF及其在惯导系统初始对准中的应用

容积卡尔曼滤波(CKF)是常用的惯性导航系统(INS)初始对准算法。针对在模型失配和观测噪声干扰情况下常规容积卡尔曼滤波出现精度下降甚至发散的问题,提出了一种自适应渐消滤波算法,引入多重渐消因子对预测误差协方差阵进行调整。设计了基于滤波残差序列统计特性的滤波状态x~2检验条件,检测滤波器故障并确定是否引入渐消因子,使渐消因子的引入时机更加合理,有效增强了算法的自适应性。仿真试验表明,新算法可以有效提高初始对准精度及鲁棒性。

Strong tracking cubature Kalman filter for initial alignment of inertial navigation system

The filtering accuracy of the cubature Kalman filter is tend to decrease or even diverse when there are disturbances of inaccurate model and/or observation noise. To solve this problem, an improved adaptive fading filter is proposed, which introduce multiple fading factors to adjust the covariance matrix of the prediction errors. A chi-square test method is designed to check the filter's fault and determine at what time the fading factors are introduced, thus the introduction of the fading factor is more reasonable, and the algorithm's adaptability is enhanced. Simulation and experiment are made for the nonlinear initial alignment of SINS suffered from complex observation noise interference, and the results show that the proposed algorithm can effectively improve the accuracy and robustness of the initial alignment.