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多变量自回归信号信息融合辨识方法 预览

Information fusion identification method for multivariable autoregressive signal
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摘要 近年来,为了提高系统模型和状态估计的精度,多传感器数据融合引起了广泛关注。对于带白色公共干扰噪声和有色观测噪声的多传感器多变量自回归(AR)模型,当AR模型参数和噪声方差未知时,提出了一种信息融合多段辨识方法,其中采用多维递推辅助变量(MRIV)方法得到AR模型参数的局部和融合估值器,再用相关方法得到局部和融合噪声方差估值器。这些估值器具有一致性,通过一个信号仿真例子验证了其有效性。 In order to improve the accuracy of system model and state estimation, the data fusion of multisensor has received great attention in recent years. For the multisensor multivariable autoregressive(AR) model with white common dis turbance noise and colored observation noise, when the AR model parameters and noise variances are unknown, an information fusion multi-stage identification method is presented, where the local and fused estimators of the AR model parameters are obtained by the multidimensional recursive instrumental variable (MRIV) algorithm, and the local and fused estimators of the noise variances are obtained by the correlation method. These estimators have consistency. A signal simulation example shows its effectiveness.
作者 陶贵丽 刘文强 黄妍 顾泽元 TAO Gui-li , LIU Wen-qiang , HUANG Yan , GU Ze-yuan (1. Heilongjiang Institute of Science and Technology, Harbin 150027, China; 2. Heilongiiang University, Harbin 150080, China)
出处 《现代电子技术》 2012年第3期 135-137,140,共4页 Modern Electronic Technique
基金 黑龙江省教育厅科学技术研究项目(11553101)
关键词 多变量AR模型 信息融合多段辨识方法 多重递推辅助变量法 信息融合估值器 一致性 multivariable AR mode information fusion multi-stage identification method multidimensional recursive instrumental variable algorithm information fusion estimator consistency
作者简介 陶贵丽 女,满族,1980年出生,黑龙江哈尔滨人,博士研究生,讲师。主要研究领域为状态估计、信息融合。
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