Add to ORKGCompressed sensing (CS) deals with the reconstruction of sparse signals from a small number of linear measurements. One of the main challenges in CS is to find the support of a sparse signal from a set of noisy observations. In the CS literature, several information-theoretic bounds on the scaling law of the required number of measurements for exact support recovery have been derived, where the focus is mainly on random measurement matrices. In this paper, we investigate the support recovery problem from an estimation theory point of view, where no specific assumption is made on the underlying measurement matrix. By using the Hammersley-Chapman-Robbins (HCR) bound, we derive a fundamental lower bound on the performance of any unbiased estim...
In the theory of compressed sensing (CS), the sparsity ‖x‖0 of the unknown signal x ∈ Rp is commonly...
Recently, great strides in sparse approximation theory and its application have been made. Many of t...
Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using...
Compressed sensing (CS) deals with the reconstruction of sparse signals from a small number of linea...
Compressed sensing (CS) deals with the reconstruction of sparse signals from a small number of linea...
In this paper we derive information theoretic performance bounds to sensing and reconstruction of sp...
Recovery of the sparsity pattern (or support) of an unknown sparse vector from a limited number of n...
In this paper, we analyze the information theoretic lower bound on the necessary number of samples n...
This paper considers the problem of sparse signal recovery when the decoder has prior information on...
This paper considers the problem of sparse signal recovery when the decoder has prior information on...
This paper considers the problem of sparse signal recovery when the decoder has prior information on...
The problem of recovering sparse signals from a limited number of measurements is now ubiquitous in ...
This paper studies the stability of some reconstruction algorithms for compressed sensing in terms o...
Abstract—Recent breakthrough results in compressive sensing (CS) have established that many high dim...
Recently, great strides in sparse approximation theory and its application have been made. Many of t...
In the theory of compressed sensing (CS), the sparsity ‖x‖0 of the unknown signal x ∈ Rp is commonly...
Recently, great strides in sparse approximation theory and its application have been made. Many of t...
Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using...
Compressed sensing (CS) deals with the reconstruction of sparse signals from a small number of linea...
Compressed sensing (CS) deals with the reconstruction of sparse signals from a small number of linea...
In this paper we derive information theoretic performance bounds to sensing and reconstruction of sp...
Recovery of the sparsity pattern (or support) of an unknown sparse vector from a limited number of n...
In this paper, we analyze the information theoretic lower bound on the necessary number of samples n...
This paper considers the problem of sparse signal recovery when the decoder has prior information on...
This paper considers the problem of sparse signal recovery when the decoder has prior information on...
This paper considers the problem of sparse signal recovery when the decoder has prior information on...
The problem of recovering sparse signals from a limited number of measurements is now ubiquitous in ...
This paper studies the stability of some reconstruction algorithms for compressed sensing in terms o...
Abstract—Recent breakthrough results in compressive sensing (CS) have established that many high dim...
Recently, great strides in sparse approximation theory and its application have been made. Many of t...
In the theory of compressed sensing (CS), the sparsity ‖x‖0 of the unknown signal x ∈ Rp is commonly...
Recently, great strides in sparse approximation theory and its application have been made. Many of t...
Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using...