International audienceThis paper is devoted to two classical sparse problems in array processing: Channel estimation and DOA estimation. It is shown after some background and some recent results in l0 optimization how this latter can be used, at the same computational cost, in order to obtain improvement in comparison with l1 optimization for sparse estimation
The use of sparsity has emerged in the last fifteen years as an important tool for solving many prob...
International audienceSparse approximation addresses the problem of approximately fitting a linear m...
ABSTRACT The least mean squares (LMS) algorithm is one of the most popular recursive parameter estim...
International audienceThis paper is devoted to two classical sparse problems in array processing: Ch...
International audienceOn-grid based direction-of-arrival (DOA) estimation methods rely on the resolu...
International audienceThis paper deals with the problem of recovering a sparse unknown signal from a...
This paper considers constrained lscr1 minimization methods in a unified framework for the recovery ...
A joint-optimization method is proposed for enhancing the behavior of the l 1 -norm- and sum-...
A vector or matrix is said to be sparse if the number of non-zero elements is significantly smaller ...
We propose a smooth approximation l0-norm constrained affine projection algorithm (SL0-APA) to impro...
This paper introduces a novel approach for recovering sparse signals using sorted L1/L2 minimization...
International audienceThis paper investigates the problem of designing a deterministic system matrix...
PhDThe significance of sparse representations has been highlighted in numerous signal processing ap...
The paper proposes a subspace based blind sparse channel estimation method using 1–2 optimization by...
L’approximation parcimonieuse consiste à ajuster un modèle de données linéaire au sens des moindres ...
The use of sparsity has emerged in the last fifteen years as an important tool for solving many prob...
International audienceSparse approximation addresses the problem of approximately fitting a linear m...
ABSTRACT The least mean squares (LMS) algorithm is one of the most popular recursive parameter estim...
International audienceThis paper is devoted to two classical sparse problems in array processing: Ch...
International audienceOn-grid based direction-of-arrival (DOA) estimation methods rely on the resolu...
International audienceThis paper deals with the problem of recovering a sparse unknown signal from a...
This paper considers constrained lscr1 minimization methods in a unified framework for the recovery ...
A joint-optimization method is proposed for enhancing the behavior of the l 1 -norm- and sum-...
A vector or matrix is said to be sparse if the number of non-zero elements is significantly smaller ...
We propose a smooth approximation l0-norm constrained affine projection algorithm (SL0-APA) to impro...
This paper introduces a novel approach for recovering sparse signals using sorted L1/L2 minimization...
International audienceThis paper investigates the problem of designing a deterministic system matrix...
PhDThe significance of sparse representations has been highlighted in numerous signal processing ap...
The paper proposes a subspace based blind sparse channel estimation method using 1–2 optimization by...
L’approximation parcimonieuse consiste à ajuster un modèle de données linéaire au sens des moindres ...
The use of sparsity has emerged in the last fifteen years as an important tool for solving many prob...
International audienceSparse approximation addresses the problem of approximately fitting a linear m...
ABSTRACT The least mean squares (LMS) algorithm is one of the most popular recursive parameter estim...