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Distributed optimal component fusion deconvolution filtering

机译：分布式最优分量融合反卷积滤波

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摘要

Using the state and white noise filters, a distributed optimal fusion deconvolution filter weighted by scalars is given for every signal component of discrete multichannel autoregressive moving average (ARMA) signal measured by multiple sensors. Under scalar weighting condition, it is optimal in the linear minimum variance (LMV) sense. Every signal component is estimated by scalar weighting fusion from local filters of the same component. The fusion filter of every signal component has higher precision than any local filter of the corresponding signal component does. Compared with the fusion filter weighted by matrices, it can reduce the computational burden since it only requires the computation of scalar weights. The signal filtering error cross-covariance between any two sensors is derived. Applying it to a double-channel signal system with three sensors shows the effectiveness.
机译：使用状态滤波器和白噪声滤波器，对由多个传感器测量的离散多通道自回归移动平均值（ARMA）信号的每个信号分量，给出了由标量加权的分布式最优融合反卷积滤波器。在标量加权条件下，它在线性最小方差（LMV）意义上是最佳的。每个信号分量都是通过标量加权融合从同一分量的局部滤波器中估算出来的。每个信号分量的融合滤波器比相应信号分量的任何局部滤波器具有更高的精度。与仅通过矩阵加权的融合滤波器相比，由于只需要标量权重的计算，因此可以减轻计算负担。得出任意两个传感器之间的信号滤波误差互协方差。将其应用于具有三个传感器的双通道信号系统显示了其有效性。

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