Adaptive filtering for complex-valued data plays a key role in the field of signal processing. So far, there has been very little research for the adaptive filtering in complex-valued errors-in-variables (EIV) model. Compared with the complex correntropy, the total complex correntropy has shown superior performance in the EIV model. However, the current gradient based maximum total complex correntropy (MTCC) adaptive filtering algorithm has suffered from the tradeoff between fast convergence rate and low weight error power. In order to improve the performance of MTCC, we develop a fixed point maximum total complex correntropy (FP-MTCC) adaptive filtering algorithm in this study. The convergence analysis of the FP-MTCC is also provided in the paper. Furthermore, we develop two recursive FP-MTCC (RFP-MTCC) algorithms for the online adaptive filtering and provide the transient analysis of RFP-MTCC. Finally, the validity of the convergence and the superiority of the proposed algorithms are verified by simulations.

中文翻译：

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自适应滤波器的定点最大总复数熵

复数值数据的自适应滤波在信号处理领域起着关键作用。到目前为止，对于复数值变量错误（EIV）模型中的自适应滤波的研究还很少。与复数熵相比，总复数熵在EIV模型中表现出优越的性能。然而，基于电流梯度的最大总复数熵（MTCC）自适应滤波算法已经在快速收敛速率和低权重误差功率之间进行权衡。为了提高MTCC的性能，在本研究中，我们开发了一个定点最大总复数熵（FP-MTCC）自适应滤波算法。本文还提供了FP-MTCC的收敛性分析。此外，我们为在线自适应滤波开发了两种递归FP-MTCC（RFP-MTCC）算法，并提供了RFP-MTCC的瞬态分析。最后，通过仿真验证了算法的收敛性和优越性。