Nowadays, complex Correntropy has been widely used for adaptive filtering in the complex domain. Compared with the second order statistics methods, the complex correntropy based algorithms have shown the superiority in the non-Gaussian noise, especially the impulsive noise. However, the current complex correntropy based adaptive filtering algorithms have not taken the input noise into consideration, and the performances will be deteriorated when the input signals are also corrupted by the noise. In this article, we focus on the errors-in-variables (EIV) model and propose an adaptive algorithm based on the maximum total complex correntropy (MTCC). More importantly, we present the local stability analysis and derive the theoretical weight error power. Simulation results confirm the validity of the theoretical analysis and illustrate the superior performance of the propose algorithm in the EIV cases.

中文翻译：

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自适应滤波器的最大总复数相关熵


如今，复相关熵已广泛用于复域中的自适应滤波。与二阶统计方法相比，基于复杂熵的算法在非高斯噪声，特别是脉冲噪声中表现出了优越性。然而，当前基于复数熵的自适应滤波算法没有考虑输入噪声，当输入信号也受到噪声的影响时，性能将会恶化。在本文中，我们重点关注变量误差（EIV）模型，并提出一种基于最大总复数相关熵（MTCC）的自适应算法。更重要的是，我们提出了局部稳定性分析并推导了理论重量误差功率。仿真结果证实了理论分析的有效性，并说明了该算法在 EIV 情况下的优越性能。