多传感器信息融合稳态最优Wiener反卷积滤波器
Multisensor Information Fusion Steady-State Optimal Wiener Deconvolution Filter
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摘要: 应用现代时间序列分析方法,基于ARMA新息模型和Lyapunov方程,提出了单通道ARMA信号的多传 感器信息融合稳态最优Wiener反卷积滤波器。它避免了Riccati方程,可用于设计含未知模型参数和含未知噪声方 差系统的自校正信息融合滤波器。一个仿真例子说明了其有效性。Abstract: By the modern time series analysis method, based on the AutoRegressive Moving Average(ARMA) innovation model and Lyapunov equation, a mulisensor information fusion Wiener deconvolution filter is presented for single channel ARMA signals. It avoids the Riccati equation and can be applied to design the self-tuning information fusion filter for systems with unknown model parameters and unknown variances. A simulation example shows its effectiveness.
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何友,王国宏,陆大,彭应宁.多传感器信息融合及其应用.北京:电子工业出版社,2000- 1-11.[2]Mendel J M. Lessons in Estimation Theory for Signal Processing,Communications and Control. Englewood Cliffs, New Jersey:Prentice Hall, 1995: 1 - 400.[3]邓自立,高媛,马建为.两传感器信息融合最优白噪声反卷积Wiener滤波器.科学技术与工程,2003,3(3):216-218.[4]邓自立.卡尔曼滤波与维纳滤波--现代时间序列分析方法.哈尔滨:哈尔滨工业大学出版社,2001:279-390.[5]邓自立.自校正滤波理论及其应用--现代时间序列分析方法.哈尔滨:哈尔滨工业大学出版社,2003:1-375.[6]邓自立,马建为,高媛.两传感器自校正信息融合白噪声Wiener反卷积滤波器.科学技术与工程,2003,3(4):325-327. -
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