Augmented Least Lncosh Conjugate Gradient Adaptive Filtering

The online conjugate gradient (CG) algorithm with error-correction learning discards the error after each iteration, which shows low filtering accuracy in prediction tasks. Moreover, the CG based on the mean-square error (MSE) criterion suffers from poor performance when dealing with impulsive noise. To address these issues, we first construct a novel batched augmented least lncosh model (ALLM) based on the least lncosh (Llncosh) criterion by fully using the discarded errors. Then, a new structure of adaptive filters is designed under the framework of ALLM for online application. Using the least lncosh CG (LLCG) algorithm for the design of controller, a novel robust augmented LLCG (ALLCG) adaptive filter is finally developed, which achieves better filtering performance than the traditional CG and LLCG. Simulations demonstrate the advantages of ALLCG on robustness and prediction accuracy.