This work proposes diffusion normalized least mean M-estimate algorithm based on the
modified Huber function, which can equip distributed networks with robust learning …

Sparse recursive least squares (RLS) adaptive filter algorithms achieve faster convergence
and better performance than the standard RLS algorithm under sparse systems. Existing …

This paper presents a family of robust set-membership normalized subband adaptive
filtering (RSM-NSAF) algorithms for acoustic echo cancellation (AEC). By using a new robust …

This paper proposes variable step-size-norm constraint normalized least mean square (-
NLMS) algorithms for sparse channel identification. The mean square deviation of the-NLMS …

This study proposes a variable step-size saturation affine projection algorithm (VSS-sat-
APA) robust to impulsive noise. The proposed algorithm is analytically derived from the …

We propose two sparsity-aware normalized subband adaptive filter (NSAF) algorithms by
using the gradient descent method to minimize a combination of the original NSAF cost …

Stochastic models that predict adaptive filtering algorithms performance usually employ
several assumptions in order to simplify the analysis. Although these simplifications facilitate …

Sparse system identification is often encountered in applications such as network and
acoustic echo cancellation. This work applies the sparsity promoting method to the affine …

This paper presents a new normalized least mean square (NLMS) algorithm for sparse
system identification where the input signal is corrupted by white measurement noise. The …

This paper proposes a diffusion proportionate affine projection sign algorithm for distributed
estimation of sparse vector over network. The algorithm is derived by minimizing l 1-norm …