Generalized Maximum Complex Correntropy Augmented Adaptive IIR Filtering

• Published in: Entropy (Basel, Switzerland) Vol. 24; no. 7; p. 1008
• Main Authors: Zheng, Haotian, Qian, Guobing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 21-07-2022; MDPI
• Subjects: Adaptive algorithms; Adaptive filters; Algorithms; augmented IIR; complex; Complex variables; Generalized linear models; GMCCC; IIR filters; Kernel functions; Mean square errors; non-Gaussian noise; Optimality criteria; Random noise; Random variables; Signal processing; Stability analysis; system identification
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e24071008

Augmented IIR filter adaptive algorithms have been considered in many studies, which are suitable for proper and improper complex-valued signals. However, lots of augmented IIR filter adaptive algorithms are developed under the mean square error (MSE) criterion. It is an ideal optimality criterion under Gaussian noises but fails to model the behavior of non-Gaussian noise found in practice. Complex correntropy has shown robustness under non-Gaussian noises in the design of adaptive filters as a similarity measure for the complex random variables. In this paper, we propose a new augmented IIR filter adaptive algorithm based on the generalized maximum complex correntropy criterion (GMCCC-AIIR), which employs the complex generalized Gaussian density function as the kernel function. Stability analysis provides the bound of learning rate. Simulation results verify its superiority.