Estimation of colored plant noise using Kalman filter based deconvolution

IEEE 1991 International Conference on Systems Engineering Pub Date : 1900-01-01 DOI:10.1109/ICSYSE.1991.161164

M.-H. Yoon, T. Ramabadran

引用次数: 2

Abstract

In many deconvolution problems, the signal to be estimated is modeled as the input to a known plant and assumed white. There are, however, situations in which this signal is not white. A simple iterative scheme for estimating colored sequences is presented. In this scheme, the colored plant noise is modeled as the output of a shaping filter excited by white noise. The shaping filter is considered as part of the plant while applying Mendel's minimum variance deconvolution (MVD) algorithm based on the Kalman filter to estimate the plant noise. To begin with, the shaping filter is just an identity filter. The estimated plant noise is then used to update its coefficients iteratively until the change in the coefficient values is small. The iterative scheme has been tested using simulated data under different conditions, and is found to perform quite well under certain situations.<>

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基于反卷积卡尔曼滤波的有色植物噪声估计

在许多反卷积问题中，要估计的信号被建模为已知植物的输入，并假定为白色。然而，在某些情况下，这个信号不是白色的。提出了一种估计彩色序列的简单迭代算法。在该方案中，彩色植物噪声被建模为由白噪声激励的整形滤波器的输出。将整形滤波器视为植物的一部分，采用基于卡尔曼滤波的孟德尔最小方差反卷积算法估计植物噪声。首先，整形滤波器只是一个身份滤波器。然后使用估计的植物噪声迭代更新其系数，直到系数值的变化很小。用不同条件下的模拟数据对该迭代方案进行了测试，并发现在某些情况下执行得很好。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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IEEE 1991 International Conference on Systems Engineering

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