摘要
为了提高测量更新的近似精度,将迭代卡尔曼滤波(IKF)的思想引入到UKF中,得到迭代无迹卡尔曼滤波算法(IUKF)。理论分析与仿真结果表明:IKF的引入在提高非线性近似精度的前提下并没有增加计算的复杂性;在相同数量级运算时间的条件下,其估计性能明显优于标准EKF和UKF滤波器。
In order to improve the approximation accuracy of the measurement update, the paper introduced iterated Kalman filter （IKF） into the unscented Kalman filter （UKF）, formed the iterated unscented Kalman filter algorithm （IUKF）. Theoretical analysis and simulation results show that： IUKF can improve the accuracy of non-linear approximation without increasing the complexity of calculation, so it has better performance than the standard EKF and UKF with similar computation burden.
出处
《机械工程与自动化》
2010年第5期7-9,13,共4页
Mechanical Engineering & Automation
基金
山西省自然科学基金资助项目(2009011026-2)
关键词
状态估计
非线性
无迹变换
无迹卡尔曼滤波器
state estimation
nonlinear
unscented transformation
unscented Kalman filter
