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广义系统信息融合稳态与自校正满阶Kalman滤波器
Information fusion steady-state and self-tuning full-order Kalman filters for descriptor systems
摘要点击聽60聽聽全文点击聽53聽聽投稿时间:2009/11/12聽聽最后修改时间:2010/11/7
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DOI编号聽聽
聽聽2011,28(9):1169-1174
中文关键词聽聽广义系统聽聽两段融合聽聽稳态满阶滤波器聽聽互协方差聽聽自校正滤波器
英文关键词聽聽descriptor system聽聽two-stage fusion聽聽steady-state full-order filter聽聽cross-covariance聽聽self-tuning filter
基金项目聽聽国家自然科学基金资助项目(60874062); 教育部科学技术研究重点资助项目(209038); 教育部新世纪优秀人才支持计划资助项目(NCET-10-0133).
学科分类代码聽聽
作者单位E-mail
马静黑龙江大学 电子工程学院, 数学科学学院
孙书利黑龙江大学 电子工程学院, 数学科学学院sunsl@hlju.edu.cn
中文摘要
聽聽聽聽聽聽基于线性最小方差标量加权融合算法和射影理论, 对带多个传感器和带相关噪声的广义系统, 提出了分布式标量加权融合稳态满阶Kalman滤波器. 推得了任两个传感器子系统之间的稳态满阶滤波误差互协方差阵, 其解可任选初值离线迭代计算. 所提出的稳态融合滤波器避免了每时刻计算协方差阵和融合权重, 减小了在线计算负担. 当系统含有未知模型参数时, 基于递推增广最小二乘算法和标量加权融合算法, 提出了一种两段融合自校正状态滤波器. 其中第1段融合获得未知参数的融合估计; 第2段融合获得分布式自校正融合状态滤波器. 与局部估计和加权平均融合估计相比, 所提出的标量加权融合参数估计和自校正状态估计都具有更高的精度. 仿真研究验证了其有效性.
英文摘要
聽聽聽聽聽聽Based on the fusion algorithm weighted by scalars in the linear minimum variance sense and the projection theory, a distributed fusion steady-state full-order Kalman filter weighted by scalars is presented for descriptor systems with multiple sensors and correlated noises. The cross-covariance matrix of steady-state full-order filtering errors between any two sensor subsystems is derived. The solution can be computed by iteration with any initial value off-line. The proposed steady-state fusion filter avoids computing covariance matrices and fusion weights at each time step, so the online computational burden can be reduced. When the unknown model parameters are involved in the system, a two-stage fusion self-tuning state filter is presented based on the recursive extended least squares algorithm and fusion algorithm weighted by scalars. The first-stage fusion is to obtain the fusion estimate of the unknown parameters. The second-stage fusion is to obtain the distributed self-tuning fusion state filter. Compared with local estimates and weighted-average fusion estimate, the presented scalar-weighted fusion estimates for parameters and self-tuning estimate for state both have better accuracy. Simulation research validates the effectiveness.
 
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