CLC number: TP311
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2018-04-15
Cited: 0
Clicked: 7033
Rasha Shoitan, Zaki Nossair, I. I. Ibrahim, Ahmed Tobal. Improving the reconstruction efficiency of sparsity adaptive matching pursuit based on the Wilkinson matrix[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(4): 503-512.
@article{title="Improving the reconstruction efficiency of sparsity adaptive matching pursuit based on the Wilkinson matrix",
author="Rasha Shoitan, Zaki Nossair, I. I. Ibrahim, Ahmed Tobal",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="4",
pages="503-512",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601588"
}
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%T Improving the reconstruction efficiency of sparsity adaptive matching pursuit based on the Wilkinson matrix
%A Rasha Shoitan
%A Zaki Nossair
%A I. I. Ibrahim
%A Ahmed Tobal
%J Frontiers of Information Technology & Electronic Engineering
%V 19
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%P 503-512
%@ 2095-9184
%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601588
TY - JOUR
T1 - Improving the reconstruction efficiency of sparsity adaptive matching pursuit based on the Wilkinson matrix
A1 - Rasha Shoitan
A1 - Zaki Nossair
A1 - I. I. Ibrahim
A1 - Ahmed Tobal
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 4
SP - 503
EP - 512
%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1601588
Abstract: sparsity adaptive matching pursuit (SAMP) is a greedy reconstruction algorithm for compressive sensing signals. SAMP reconstructs signals without prior information of sparsity and presents better reconstruction performance for noisy signals compared to other greedy algorithms. However, SAMP still suffers from relatively poor reconstruction quality especially at high compression ratios. In the proposed research, the wilkinson matrix is used as a sensing matrix to improve the reconstruction quality and to increase the compression ratio of the SAMP technique. Furthermore, the idea of block compressive sensing (BCS) is combined with the SAMP technique to improve the performance of the SAMP technique. Numerous simulations have been conducted to evaluate the proposed BCS-SAMP technique and to compare its results with those of several compressed sensing techniques. Simulation results show that the proposed BCS-SAMP technique improves the reconstruction quality by up to six decibels (dB) relative to the conventional SAMP technique. In addition, the reconstruction quality of the proposed BCS-SAMP is highly comparable to that of iterative techniques. Moreover, the computation time of the proposed BCS-SAMP is less than that of the iterative techniques, especially at lower measurement fractions.
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