Fixed-Point Maximum Total Complex Correntropy Algorithm for Adaptive Filter

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.

Automated summary

The study develops a fixed point maximum total complex correntropy (FP-MTCC) adaptive filtering algorithm to improve the performance of MTCC. The convergence analysis of the FP-MTCC is provided in the paper. Additionally, two recursive FP-MTCC (RFP-MTCC) algorithms are developed for online adaptive filtering with transient analysis.