Robust Generalized Maximum Blake–Zisserman Total Correntropy Adaptive Filter for Generalized Gaussian Noise and Noisy Input
Haiquan Zhao, Zian Cao
Abstract
Currently, the generalized maximum correntropy criterion (GMCC) is extensively used in adaptive filtering arithmetic to handle generalized Gaussian noise. However, when the input signal is also subject to noise interference, the performance of the GMCC algorithm is degraded. In addition, the algorithms developed based on GMCC standards are facing high steady-state error problems. To address these issues, the generalized maximum Blake–Zisserman total correntropy (GMBZTC) algorithm based on the generalized maximum Blake–Zisserman (GMBZ) robust loss function is proposed in this article. More importantly, this article gives a detailed performance evaluation of the GMBZTC algorithm under generalized Gaussian noise conditions, obtains the conditions that guarantee the stability of the algorithm, and calculates the steady-state mean square deviation (S-MSD) of the algorithm. Finally, the excellence of the GMBZTC algorithm compared with other algorithms and the correctness of the theoretical analysis are demonstrated by simulation.