多傳感器信息融合穩(wěn)態(tài)最優(yōu)Wiener反卷積濾波器
Multisensor Information Fusion Steady-State Optimal Wiener Deconvolution Filter
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摘要: 應(yīng)用現(xiàn)代時間序列分析方法,基于ARMA新息模型和Lyapunov方程,提出了單通道ARMA信號的多傳 感器信息融合穩(wěn)態(tài)最優(yōu)Wiener反卷積濾波器。它避免了Riccati方程,可用于設(shè)計含未知模型參數(shù)和含未知噪聲方 差系統(tǒng)的自校正信息融合濾波器。一個仿真例子說明了其有效性。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,王國宏,陸大,彭應(yīng)寧.多傳感器信息融合及其應(yīng)用.北京:電子工業(yè)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]鄧自立,高媛,馬建為.兩傳感器信息融合最優(yōu)白噪聲反卷積Wiener濾波器.科學(xué)技術(shù)與工程,2003,3(3):216-218.[4]鄧自立.卡爾曼濾波與維納濾波--現(xiàn)代時間序列分析方法.哈爾濱:哈爾濱工業(yè)大學(xué)出版社,2001:279-390.[5]鄧自立.自校正濾波理論及其應(yīng)用--現(xiàn)代時間序列分析方法.哈爾濱:哈爾濱工業(yè)大學(xué)出版社,2003:1-375.[6]鄧自立,馬建為,高媛.兩傳感器自校正信息融合白噪聲Wiener反卷積濾波器.科學(xué)技術(shù)與工程,2003,3(4):325-327. -
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