The maximum Versoria criterion algorithm (MVC) exhibits lower steady-state misalignment
and less complexity as compared to the maximum correntropy criterion (MCC) algorithm in …

Orthogonal least squares (OLS) is a popular greedy algorithm because of its simplicity and
low complexity. To improve the reconstruction accuracy performance of OLS, the multiple …

In magnetic resonance (MR) image reconstruction, the orthogonal matching pursuit (OMP) is
widely recognized for its simplicity and competitive performance. However, OMP designs a …

Less sensitivity to the sparsity of the underwater acoustic (UWA) channel is a drawback of
the improved proportionate normalized least-mean-square (IPNLMS) algorithm. To address …

The conventional methods including cross-ambiguity function (CAF) and least square (LS)
methods have intensively studied for the estimation of the delay-Doppler parameters from …

This work deals with the block-sparse system identification problem on the basis of the
maximum correntropy criterion (MCC). The MCC is known for its robustness against non …

In this work, an effective adaptive block-sparsity log-sum least mean square (BSLS-LMS)
algorithm is proposed to improve the convergence performance of cluster sparse system …

This study proposes a scaler for the normalized least-mean-square algorithm, which is
derived based on a cost function designed to retain robustness to a sudden change. A novel …

This paper presents a novel approach for structure extraction of the cluster sparse system
identification. Different from adopting ℓ 1-norm constraint to regularize the sparsity in the …

Recovering sparse signals from compressed measurements has received much attention in
recent years. Considering that measurement errors always exist, an improved orthogonal …