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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