QQ客服 官方微博 反馈留言

	Analyzing of the fusion estimation algorithm of a class of dynamic multiscale system
	Cui Pei-ling ; Wang Gui-zeng ; Pan Quan
	2010-05-06 ; 2010-05-06
关键词	Theoretical or Mathematical/ estimation theory Kalman filters state-space methods/ fusion estimation algorithm dynamic multiscale system state space projection Kalman filter general time registration method/ B0240Z Other topics in statistics B6140B Filtering methods in signal processing C1140Z Other topics in statistics C1260S Signal processing theory
中文摘要	The fusion estimation algorithm of a class of dynamic multiscale system based on state space projection is explained for clearly understanding of its essence in this paper. Firstly, the fusion estimation algorithm is compared with that directly performing Kalman filter at the finest scale. Simulation results show that performance of the fusion estimation algorithm outperforms that of directly performing Kalman filter at the finest scale. Secondly, the fusion estimation algorithm is compared with general time registration method in terms of algorithm process and computational cost. It is shown that, time registration is replaced with rigorous mathematical model in the fusion estimation algorithm, and optimal estimation based on measurement information at all scales can be obtained, and at the same time, the computational cost is larger than that of time registration method. It thus lays a foundation for practical application of the fusion estimation algorithm for a class of dynamic multiscale system.
语种	中文 ; 中文
出版者	South China Univ. Technol ; China
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/9249]
专题	清华大学
推荐引用方式 GB/T 7714	Cui Pei-ling,Wang Gui-zeng,Pan Quan. Analyzing of the fusion estimation algorithm of a class of dynamic multiscale system[J],2010, 2010.
APA	Cui Pei-ling,Wang Gui-zeng,&Pan Quan.(2010).Analyzing of the fusion estimation algorithm of a class of dynamic multiscale system..
MLA	Cui Pei-ling,et al."Analyzing of the fusion estimation algorithm of a class of dynamic multiscale system".(2010).

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。