强跟踪CKF及其在惯导系统初始对准中的应用

发布时间：2018-04-30 12:54

本文选题：容积卡尔曼滤波 + 惯性导航系统　；参考：《中国惯性技术学报》2017年04期

【摘要】：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。
[Abstract]:Volume Kalman filter (CKF) is a commonly used inertial navigation system (ins) initial alignment algorithm. In order to solve the problem that the accuracy of conventional volumetric Kalman filter decreases or even diverges under the condition of model mismatch and observation noise interference, an adaptive filtering algorithm is proposed, in which multiple fading factors are introduced to adjust the prediction error covariance matrix. Based on the statistical characteristics of filter residuals, the filter state x2 test condition is designed to detect filter faults and determine whether to introduce fading factor, so that the timing of introducing fading factor is more reasonable, and the self-adaptability of the algorithm is effectively enhanced. Simulation results show that the new algorithm can effectively improve the initial alignment accuracy and robustness.
【作者单位】：海军工程大学导航工程系;
【基金】：国家自然科学基金(41574069);国家自然科学基金(61503404);国家自然科学基金(41404002)
【分类号】：TN713;TN96

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