检索结果-南通市图书馆

第三十届中国控制会议

作者： hu zhen-tao,liu xian-xing~**,jin yong laboratory of image processing & pattern recognition,henan university,kaifeng 475001,henan,p.r.china

＜正＞Aiming at the effective approximation of sampling particle set relative to system state in observation uncertainty,a novel cost reference particle filter based on adaptive particle swarm optimization is *** the new algorithm,the cost function and the risk function are firstly introduced to realize reasonable utilization of the latest *** addition, according to the prior modeling information,a new adaptive method is given to solve the selection of limit *** then the movement of particle set towards the region of high weight particle is completed by particle swarm optimization *** algorithm realizes the dynamic combination of the cost reference particle filter and the adaptive particle swarm optimization,and the reliability and stabilize of sampling particle set relative to system state are *** theoretical analysis and experimental results show the efficiency of the proposed algorithm.

关键词： Nonlinear Filter Cost reference particle Filter particle Swarm Optimization Observation Uncertainty

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

A novel multi-sensor multiple model particle filter

A novel multi-sensor multiple model particle filter

引用

第三十一届中国控制会议

作者： hu zhen-tao liu xian-xing~** LI Jie Institute of image processing and pattern recognition henan university kaifeng 47500 p.r.china

＜正＞The large amount of calculation always severely restricts the application domain expansion of particle filter,a novel multi-sensor multiple model particle filtering algorithm based on particle weight optimization is *** the multiple model particle filter framework,the optimization method of particle weight is realized by the extraction and utilization of redundancy and complementary information from latest multi-sensor *** to weaken the adverse influence of random observations noise,the stability and reliability is effective *** theoretical analysis and experimental results show that the new algorithm can improve the filter precision but also lessens computational burden in nonlinear system estimation with multi-sensor multiple model characteristic.

关键词： Multiple Model particle Filter particle Weight Optimization Weighting Fusion Interacting Multiple Model

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Distributed cubature Kalman filter based on observation bootstrap sampling

引用

High Technology Letters 2016年第2期22卷 142-147页

作者：胡振涛 hu Yumei zheng Shanshan Li xian Guo zhen Institute of image processing and pattern recognition Henan University

Aiming at the adverse effect caused by observation noise on system state estimation precision,a novel distributed cubature Kalman filter(CKF) based on observation bootstrap sampling is ***,combining with the extraction and utilization of the latest observation information and the prior statistical information from observation noise modeling,an observation bootstrap sampling strategy is *** objective is to deal with the adverse influence of observation uncertainty by increasing observations ***,the strategy is dynamically introduced into the cubature Kalman filter,and the distributed fusion framework of filtering realization is *** filtering precision is obtained by promoting observation reliability without increasing the hardware cost of observation *** analysis and simulation results show the proposed algorithm feasibility and effectiveness.

关键词： Bootstrap 卡尔曼滤波器抽样容积观测噪声观测信息分布式融合估计精度

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：