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- 2018
自适应渐消卡尔曼滤波及其在SINS初始对准中的应用
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Abstract:
卡尔曼滤波常常被用于惯性导航系统初始对准算法，其使用前提是对系统状态进行建模，从而得到比较准确的系统噪声和观测噪声统计特性。在模型失配和观测噪声干扰的情况下，常规卡尔曼滤波会出现精度下降甚至发散，从而影响初始对准精度。针对这一问题，提出了一种新型渐消卡尔曼滤波算法，引入了多重渐消因子对预测误差协方差阵进行调整，设计了基于新息向量统计特性的滤波状态χ2检验条件，使渐消因子的引入时机更加合理，算法的自适应性得到增强。将改进的卡尔曼滤波算法应用到惯性导航系统的初始对准问题中，仿真试验和实测数据试验结果表明，与常规渐消因子滤波算法相比，新算法可以有效提高滤波精度及鲁棒性
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