Abstract
A key point for the recovery of a block-sparse signal is how to treat the different sparsity distributed on the different parts of the considered signal. It has been shown recently that grouping the signal, i.e., partitioning the original signal into different groups or segments, and conducting the recovery for these groups separately provides an effective method to deal with the block-structured sparsity and can generate much better performance than the conventional sparse signal recovery (SSR) algorithms. In order to further improve the recovery performance, instead of the fixed grouping method used in the recent results, a novel dynamic grouping method will be first proposed in this paper, which classifies the segments due to the different levels of sparsity in a dynamic way. Then, by incorporating this technique into the block version of adaptive SSR algorithms. we developed recently, i.e., the block zero-attracting least-mean-square (BZA-LMS) algorithm and the block ℓ0-norm LMS (B ℓ0-LMS) algorithm, the corresponding new algorithms, i.e., the BZA-LMS-D and B ℓ0-LMS-D algorithms, will be established. The performance superiorities and the robustness against different block-sparsity and/or noise interference for the new algorithms based on dynamic grouping will be demonstrated by both analytic discussions and numerical simulations for a variety of scenarios.
| Original language | English |
|---|---|
| Article number | 8476546 |
| Pages (from-to) | 56069-56083 |
| Number of pages | 15 |
| Journal | IEEE Access |
| Volume | 6 |
| DOIs | |
| Publication status | Published - 2018 Jan 1 |
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Keywords
- Adaptive filter
- Block-sparsity
- Compressive sensing
- Sparse constraint
ASJC Scopus subject areas
- Computer Science(all)
- Materials Science(all)
- Engineering(all)
Cite this
Recovery of block-structured sparse signal using block-sparse adaptive algorithms via dynamic grouping. / Ye, Chen; Gui, Guan; Xu, Li; Ohtsuki, Tomoaki.
In: IEEE Access, Vol. 6, 8476546, 01.01.2018, p. 56069-56083.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Recovery of block-structured sparse signal using block-sparse adaptive algorithms via dynamic grouping
AU - Ye, Chen
AU - Gui, Guan
AU - Xu, Li
AU - Ohtsuki, Tomoaki
PY - 2018/1/1
Y1 - 2018/1/1
N2 - A key point for the recovery of a block-sparse signal is how to treat the different sparsity distributed on the different parts of the considered signal. It has been shown recently that grouping the signal, i.e., partitioning the original signal into different groups or segments, and conducting the recovery for these groups separately provides an effective method to deal with the block-structured sparsity and can generate much better performance than the conventional sparse signal recovery (SSR) algorithms. In order to further improve the recovery performance, instead of the fixed grouping method used in the recent results, a novel dynamic grouping method will be first proposed in this paper, which classifies the segments due to the different levels of sparsity in a dynamic way. Then, by incorporating this technique into the block version of adaptive SSR algorithms. we developed recently, i.e., the block zero-attracting least-mean-square (BZA-LMS) algorithm and the block ℓ0-norm LMS (B ℓ0-LMS) algorithm, the corresponding new algorithms, i.e., the BZA-LMS-D and B ℓ0-LMS-D algorithms, will be established. The performance superiorities and the robustness against different block-sparsity and/or noise interference for the new algorithms based on dynamic grouping will be demonstrated by both analytic discussions and numerical simulations for a variety of scenarios.
AB - A key point for the recovery of a block-sparse signal is how to treat the different sparsity distributed on the different parts of the considered signal. It has been shown recently that grouping the signal, i.e., partitioning the original signal into different groups or segments, and conducting the recovery for these groups separately provides an effective method to deal with the block-structured sparsity and can generate much better performance than the conventional sparse signal recovery (SSR) algorithms. In order to further improve the recovery performance, instead of the fixed grouping method used in the recent results, a novel dynamic grouping method will be first proposed in this paper, which classifies the segments due to the different levels of sparsity in a dynamic way. Then, by incorporating this technique into the block version of adaptive SSR algorithms. we developed recently, i.e., the block zero-attracting least-mean-square (BZA-LMS) algorithm and the block ℓ0-norm LMS (B ℓ0-LMS) algorithm, the corresponding new algorithms, i.e., the BZA-LMS-D and B ℓ0-LMS-D algorithms, will be established. The performance superiorities and the robustness against different block-sparsity and/or noise interference for the new algorithms based on dynamic grouping will be demonstrated by both analytic discussions and numerical simulations for a variety of scenarios.
KW - Adaptive filter
KW - Block-sparsity
KW - Compressive sensing
KW - Sparse constraint
UR - http://www.scopus.com/inward/record.url?scp=85054378684&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054378684&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2018.2872671
DO - 10.1109/ACCESS.2018.2872671
M3 - Article
AN - SCOPUS:85054378684
VL - 6
SP - 56069
EP - 56083
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 8476546
ER -