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CLC number: TP311

On-line Access: 2018-06-07

Received: 2016-09-27

Revision Accepted: 2017-03-21

Crosschecked: 2018-04-15

Cited: 0

Clicked: 1739

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Rasha Shoitan

http://orcid.org/0000-0003-0372-4293

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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.4 P.503-512

http://doi.org/10.1631/FITEE.1601588


Improving the reconstruction efficiency of sparsity adaptive matching pursuit based on the Wilkinson matrix


Author(s):  Rasha Shoitan, Zaki Nossair, I. I. Ibrahim, Ahmed Tobal

Affiliation(s):  Computer and Systems Department, Electronic Research Institute, Giza 12622, Egypt; more

Corresponding email(s):   Rasha.shoitan@eri.sci.eg

Key Words:  Block compressive sensing, Sparsity adaptive matching pursuit, Greedy algorithm, Wilkinson matrix


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.

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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.

基于Wilkinson矩阵提升稀疏自适应匹配追踪重构效率

摘要:稀疏自适应匹配追踪(sparsity adaptive matching pursuit, SAMP)是压缩感知信号的一种贪婪重构算法。SAMP可以在没有稀疏先验信息的情况下重构信号,与其他贪婪算法相比对噪声信号具有更好的重构性能。但SAMP在重建质量方面,特别是在高压缩比时,仍有不足。采用Wilkinson矩阵作为感测矩阵,以提高重建质量并增加SAMP技术的压缩比。将块压缩感知(block compressive sensing, BCS)思想与SAMP技术结合,以提高SAMP技术性能。通过大量试验对所提出BCS-SAMP技术进行评估,并将其结果与其他几种压缩传感技术结果作比较。结果表明,BCS-SAMP技术与传统SAMP技术相比,重建质量可提升6分贝(dB)。BCS-SAMP技术在重建质量方面与迭代技术相比大致接近,该技术计算时间小于迭代技术计算时间,这一优势在测量分数较低时更为明显。

关键词:块压缩传感;稀疏自适应匹配追踪;贪婪算法;Wilkinson矩阵

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