A sequential adaptive compressed sensing procedure for signal support recovery is proposed and analyzed. The procedure is based on the principle of distilled sensing, and makes used of sparse sensing matrices to perform sketching observations able to quickly identify irrelevant signal components. It is shown that adaptive compressed sensing enables recovery of weaker sparse signals than those that can be recovered using traditional non-adaptive compressed sensing approaches
Sparsity is at the heart of numerous applications dealing with multidimensional phenomena with low-i...
Abstract- Compressed Sensing (CS) is an emerging signal acquisition theory that provides a universal...
Adaptive sampling results in significant improvements in the recovery of sparse signals in white Gau...
A sequential adaptive compressed sensing procedure for signal support recovery is proposed and analy...
The theory of compressed sensing shows that sparse signals in high-dimensional spaces can be recover...
The recently-proposed theory of distilled sensing establishes that adaptivity in sampling can dramat...
This paper investigates the problem of recovering the support of structured signals via adaptive com...
Abstract—The recently-proposed theory of distilled sensing establishes that adaptivity in sampling c...
This paper investigates the problem of estimating the support of structured signals via adaptive com...
Abstract—The recently-proposed theory of distilled sensing establishes that adaptivity in sampling c...
A selective sampling procedure called distilled sensing (DS) is proposed, and shown to be an effecti...
Sparsity is at the heart of numerous applications dealing with multidimensional phenomena with low-i...
Abstract- Compressed Sensing (CS) is an emerging signal acquisition theory that provides a universal...
Adaptive sampling results in significant improvements in the recovery of sparse signals in white Gau...
A sequential adaptive compressed sensing procedure for signal support recovery is proposed and analy...
The theory of compressed sensing shows that sparse signals in high-dimensional spaces can be recover...
The recently-proposed theory of distilled sensing establishes that adaptivity in sampling can dramat...
This paper investigates the problem of recovering the support of structured signals via adaptive com...
Abstract—The recently-proposed theory of distilled sensing establishes that adaptivity in sampling c...
This paper investigates the problem of estimating the support of structured signals via adaptive com...
Abstract—The recently-proposed theory of distilled sensing establishes that adaptivity in sampling c...
A selective sampling procedure called distilled sensing (DS) is proposed, and shown to be an effecti...
Sparsity is at the heart of numerous applications dealing with multidimensional phenomena with low-i...
Abstract- Compressed Sensing (CS) is an emerging signal acquisition theory that provides a universal...
Adaptive sampling results in significant improvements in the recovery of sparse signals in white Gau...