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	Multi-layer unscented Kalman filtering algorithm based on Gaussian distribution
	Liu, Jiang1; Wang, Yu-Jin2; Duan, Jian-Lei2; Ye, Song-Qing1
	2016
摘要	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. © 2016, Editorial Office of Control and Decision. All right reserved.
DOI	10.13195/j.kzyjc.2015.0039
发表期刊	Kongzhi yu Juece/Control and Decision
ISSN	10010920
卷号	31期号:4页码:609-615
通讯作者	Wang, Yu-Jin (wangyujin@cigit.ac.cn)
收录类别	EI
语种	中文