摘要
Sage-Husa自适应卡尔曼滤波算法是解决系统模型发生变化的捷联惯导系统初始对准的一种有效工具,但对未知的系统噪声方差阵和观测噪声方差阵进行同时估计将会造成滤波发散.本文将在选择最佳遗忘因子的基础上,选取仅对观测噪声方差阵和均值进行估计的改造Sage-Husa自适应卡尔曼滤波算法进行捷联惯导系统的初始对准.计算机仿真试验结果表明:该方法在收敛速度和精度上都有很大改进.
Sage-Husa model error. But it is and R simultaneously. adaptive kalman filter improves the precision adaptive kalman filter algorithm is useful tool in initial alignment of SINS with to make the filter diverge that Sage-Husa adaptive kalman filter estimates Q Based on choosing the best fading factor, the paper gives a changed Sage-Husa algorithm with only estimating R. The simulation results show that the algorithm and the speed of convergence.
