In this overview article, we review the literature on design and analysis of recursive
algorithms for reconstructing a time sequence of sparse signals from compressive …

Conventional phased-array space-time adaptive processing (STAP) radar combines angle
and Doppler domains to realize clutter suppression. However, it suffers from severe …

Novel constructive and destructive parsimonious extreme learning machines (CP-and DP-
ELM) are proposed in this paper. By virtue of the proposed ELMs, parsimonious structure …

In this paper, we propose a family of low-complexity adaptive filtering algorithms based on
dichotomous coordinate descent (DCD) iterations for identification of sparse systems. The …

A new reweighted l 1-norm penalized least mean square (LMS) algorithm for sparse
channel estimation is proposed and studied in this paper. Since standard LMS algorithm …

Superresolution methods can be applied to real aperture radar (RAR) to improve its angular
resolution by solving an inverse problem. However, traditional superresolution methods are …

The l 1-norm sparsity constraint is a widely used technique for constructing sparse models.
In this paper, two zeroattracting recursive least squares algorithms, which are referred to as …

In this paper, we derive an online estimator for sparse parameter vectors which, unlike the
LASSO approach, does not require the tuning of any hyperparameters. The algorithm is …

In this letter, we propose a novel conjugate gradient (CG) adaptive filtering algorithm for
online estimation of system responses that admit sparsity. Specifically, the Sparsity …

We consider the problem of estimating the sparse time-varying parameter vectors of a point
process model in an online fashion, where the observations and inputs respectively consist …