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题名:
A new hybrid grid multiple model estimation based on STF
作者: Yang, Liu1,2; Zhao, Ma3; Qinzhang, Wu1
刊名: Journal of Computers
出版日期: 2012
卷号: 7, 期号:7, 页码:1607-1614
学科分类: Target tracking
DOI: 10.4304/jcp.7.7.1607-1614
通讯作者: Yang, L. (liuyang_xueshu2009@163.com)
文章类型: 期刊论文
中文摘要: The paper presents a hybrid model grid variable structure multiple model algorithm basing on strong tracking filter (STF-VSMM) which is used to state estimation for complicated system. The total model set for STF-VSMM is the combination of a coarse model grid and an adaptive fine grid moving freely in the system mode space. During the produce of the fine model grid, the paper uses a strong tracking filter to get the center position. Then, STF-VSMM can form a two-double bestrow of the system mode space. At last, the paper realizes the accurate tracking of maneuvering target using STF-VSMM. Simulation results demonstrate that STF-VSMM estimator outperforms the corresponding fixed structure multiple model (FSMM) at a negligible extra computational cost. © 2012 ACADEMY PUBLISHER.
英文摘要: The paper presents a hybrid model grid variable structure multiple model algorithm basing on strong tracking filter (STF-VSMM) which is used to state estimation for complicated system. The total model set for STF-VSMM is the combination of a coarse model grid and an adaptive fine grid moving freely in the system mode space. During the produce of the fine model grid, the paper uses a strong tracking filter to get the center position. Then, STF-VSMM can form a two-double bestrow of the system mode space. At last, the paper realizes the accurate tracking of maneuvering target using STF-VSMM. Simulation results demonstrate that STF-VSMM estimator outperforms the corresponding fixed structure multiple model (FSMM) at a negligible extra computational cost. © 2012 ACADEMY PUBLISHER.
收录类别: Ei
语种: 英语
ISSN号: 1796203X
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.ioe.ac.cn/handle/181551/4403
Appears in Collections:光电探测技术研究室(三室)_期刊论文

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作者单位: 1. The Institute of Optics and Electronics, The Chinese Academy of Science, Chengdu 610209, China
2. The Chinese Academy of Science, Beijing 100049, China
3. Henan provincial industrial information and standard research institute, Zhengzhou 475000, China

Recommended Citation:
Yang, Liu,Zhao, Ma,Qinzhang, Wu. A new hybrid grid multiple model estimation based on STF[J]. Journal of Computers,2012,7(7):1607-1614.
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