Blind identification of Volterra-Hammerstein systems
Blind identification of Volterra-Hammerstein systems
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DOI：
10.1109/tsp.2005.850357
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发表时间：
2005-08
影响因子：
5.4
通讯作者：
N. Kalouptsidis;P. Koukoulas
中科院分区：
文献类型：
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作者：
N. Kalouptsidis;P. Koukoulas
本文研究Volterra-Hammerstein系统的盲辨识问题。两个识别场景。第一种情况假设，虽然输入不可用，但输入的统计数据是先验已知的。这种情况出现在通信应用中，其中发射机的输入统计对于接收机是已知的。第二个场景假设输入统计信息是未知的。在已知输入统计量的情况下，输入是具有任意概率密度函数的平稳高阶白色噪声。在输入统计量未知的情况下，将输入限制为高斯白色过程。新的基于累积量的识别方法被描述用于上述场景。该问题被转换为线性多变量形式，并使用克罗内克积计算输出累积量。首先，初始条件由线性方程组确定。这些对应于沃尔泰拉核的边界值。剩余的核系数可以根据两种识别方案从可能的超定线性方程组确定。
This paper is concerned with the blind identification of Volterra-Hammerstein systems. Two identification scenarios are covered. The first scenario assumes that, although the input is not available, the statistics of the input are a priori known. This case appears in communication applications where the input statistics of the transmitter are known to the receiver. The second scenario assumes that the input statistics are unknown. In the case of known input statistics, the input is stationary higher order white noise with arbitrary probability density function. Under the scenario of unknown input statistics, the input is restricted to Gaussian white process. New cumulant-based identification methods are described for the above scenarios. The problem is converted into a linear multivariable form and the output cumulants are calculated using Kronecker products. First, initial conditions are determined by a linear system of equations. These correspond to the boundary values of the Volterra kernels. The remaining kernel coefficients can be determined under both identification schemes from a possibly overdetermined system of linear equations.