基于因子图的有色高斯过程LMMSE滤波
Factor Graph Based LMMSE Filtering for Colored Gaussian Processes
摘要：
我们提出了一个低复杂度，基于图的线性最小均方误差（LMMSE）滤波器，其中考虑到随机过程的非白特性。我们的方法对应于块LMMSE滤波，并具有复杂度随块长度线性增加的优点，并易于将输入信号的先验信息尽可能。所提出的方法可以用于任何随机过程与已知的自相关函数的帮助下的近似自回归（AR）过程。我们通过大量的仿真表明，我们的方法执行非常接近高斯输入信号的最佳块LMMSE滤波。
英文摘要：
We propose a low complexity, graph based linear minimum mean square error (LMMSE) filter in which the non-white characteristics of a random process are taken into account. Our method corresponds to block LMMSE filtering, and has the advantage of complexity linearly increasing with the block length and the ease of incorporating the a priori information of the input signals whenever possible. The proposed method can be used with any random process with a known autocorrelation function with the help of an approximation to an autoregressive (AR) process. We show through extensive simulations that our method performs very close to the optimal block LMMSE filtering for Gaussian input signals.
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