摘要
在传统无迹卡尔曼滤波(UKF)中对其估计精度和计算效率起关键作用的是采样算法,即构造具有权重的样本点.研究表明,带权样本点匹配随机变量的阶矩越高滤波的精度越高,如多项式无迹卡尔曼滤波(PUKF),但通常此类算法的复杂度过高甚至难以求解.为此,基于高斯分布结合高阶矩匹配与无迹卡尔曼滤波线性扩张方法(LUKF),提出一种兼顾效率和精度的高斯滤波离线算法.实验结果表明,所提出算法拥有比UKF更高的估计精度和比PUKF更好的计算效率.
The sampling algorithm of unscented Kalman filter（UKF）, which selects the sigma points and their weights,plays a vital role for the accuracy and computational efficiency. It is well known that, more moments of random variables are matched, more accuracy reaches, for example, the Polynomial-extension of UKF（PUKF）. However, such methods often suffer from their highly computational complexity, even worse, it is hard to get a solution. An efficient and highly accurate off-line algorithm is proposed for the Gaussian filter based on the high-order moments matching and linear-extension of UKF（LUKF）. Experimental results show that the proposed algorithm has more accuracy than UKF and more computational efficiency than PUKF.
出处
《控制与决策》
EI
CSCD
北大核心
2016年第4期609-615,共7页
Control and Decision
基金
国家自然科学基金项目(61202131)
重庆市科委基金项目(cstc2012gg B40004
cstc2014jcsfglyjs0005
cstc2014zktjccxyy B0031)
中国科学院"西部之光"项目
