Compressive sensing generally relies on the ℓ 2 norm for data fidelity, whereas in many
applications, robust estimators are needed. Among the scenarios in which robust …

This paper addresses the issue of robust sparse recovery in compressive sensing (CS) in
the presence of impulsive measurement noise. Recently, robust data-fitting models, such as …

An adaptive estimation algorithm based on the Lorentzian norm is proposed for echo
cancellation in vehicle hands-free communication systems and video teleconferencing …

We investigate the recovery of signals exhibiting a sparse representation in a general (ie,
possibly redundant or incomplete) dictionary that are corrupted by additive noise admitting a …

Recently, the annihilating filter-based low-rank Hankel matrix (ALOHA) approach was
proposed as a powerful image inpainting method. Based on the observation that …

This paper provides statistical analysis for efficient detection of signal components when
missing data samples are present. This analysis is important for both the areas of L-statistics …

Recent results in telecardiology show that compressed sensing (CS) is a promising tool to
lower energy consumption in wireless body area networks for electrocardiogram (ECG) …

Compressive sensing provides a framework for recovering sparse signals of length N from
M¿ N measurements. If the measurements contain noise bounded by¿, then standard …

This paper addresses the robust reconstruction problem of a sparse signal from compressed
measurements. We propose a robust formulation for sparse reconstruction that employs the …

An analysis of signal reconstruction possibility using a small set of samples corrupted by
noise is considered. False detection and/or misdetection of sparse signal components may …