Weak-Selection Backtracking Matching Pursuit Algorithm Based on Dice Coefficient

doi:10.12068/j.issn.1005-3026.2021.02.006

Abstract

Abstract: In order to further improve the success rate and accuracy of reconstruction of the compressed sensing reconstruction algorithm， a weak-selection backtracking matching pursuit based on Dice coefficient(DWBMP) algorithm is proposed from the perspective of atomic matching criteria and pre-selection stage’s atom selection methods. First， the Dice coefficient matching criterion is used to measure the similarity between two vectors， and the best matching atom is selected to optimize the support set. Then， the backtracking idea is combined with weak-selection idea to eliminate the atoms with small similarity， thus completing the secondary selection of the atoms in the pre-selection stage. The MATLAB simulation results show that under the same conditions， the DWBMP algorithm has better success rate and accuracy of reconstruction than the classic compressed sensing reconstruction algorithm.

Key words: compressed sensing; reconstruction algorithm; greedy algorithm; matching criteria; Dice coefficient; secondary selection

CLC Number:

TN911.7

JI Ce， WANG Jin-zhi， GENG Rong. Weak-Selection Backtracking Matching Pursuit Algorithm Based on Dice Coefficient[J]. Journal of Northeastern University(Natural Science), 2021, 42(2): 189-195.

References

[1]Donoho D L.Compressed sensing［J］.IEEE Transactions on Information Theory，2006，52(4):1289-1306.
[2]Candes E J，Romberg J，Tao T.Robust uncertainty principles:exact signal recognition from highly incomplete frequency information［J］. IEEE Transactions on Information Theory，2006，52(2):489-509.
[3]Candes E J.Compressive sampling［C］// Proceedings of the International Congress of Mathematicians.Madrid，2006:1433-1452.
[4]杨凯.基于压缩感知的信道估计技术研究［D］.成都:电子科技大学，2018:22-23.(Yang Kai.Research on channel estimation based on compressed sensing［D］.Chengdu:University of Electronic Science and Technology of China，2018:22-23.)
[5]Tropp J A，Gilbert A C.Signal recovery from random measurements via orthogonal matching pursuit［J］.IEEE Transactions on Information Theory，2007，53(12):4655-4666.
[6]Needell D，Vershynin R.Uniform uncertainty principle and signal recovery via regularized matching pursuit［J］.Foundations of Computational Mathematics，2009，9(3):317-334.
[7]Needell D，Tropp J A.CoSaMP:iterative signal recovery from incomplete and inaccurate samples［J/OL］.Applied and Computational Harmonic Analysis，2008，26(3):301-321(2008-04-17) ［2020-03-08］.https://arxiv.org/pdf/0803.2392v2.pdf.
[8]Dai W，Milenkovic O.Subspace pursuit for compressive sensing signal reconstruction［J］.IEEE Transactions on Information Theory，2009，55(5):2230-2249.
[9]Blumensath T，Davies M E.Stagewise weak gradient pursuit［J］.IEEE Transactions on Signal Processing，2009，57(11):4333-4346.
[10]Abdel-Sayed M M，Khattab A，Abu-Elyazeed M F.Adaptive reduced-set matching pursuit for compressed sensing recovery［C］// IEEE International Conference on Image Processing.Phoenix，2016:25-28.
[11]Zhu M D，Li M Y，Geng Z，et al.Dice coefficient matching-based sparsity adaptive matching pursuit algorithm for the digital predistortion model pruning［C］// IEEE 18th International Conference on Communication Technology.Chongqing，2018:1032-1035.
[12]王欣，张严心，黄志清.基于变步长的正则化回溯自适应追踪算法［J］.电子学报，2018，46(8):1829-1834.(Wang Xin，Zhang Yan-xin，Huang Zhi-qing.Regularized backing adaptive pursuit algorithm based variable step-size［J］.Acta Electronica Sinica，2018，46(8):1829-1834.)
[13]Hu Y F，Zhao L Q.A fuzzy selection compressive sampling matching pursuit algorithm for its practical application［J］. IEEE Access，2019，7:144101-144124.
[14]费洪涛，何雪云，梁彦.基于自适应压缩感知的OFDM稀疏信道估计研究［J］.南京邮电大学学报(自然科学版)，2019，39(2):49-54.(Fei Hong-tao，He Xue-yun，Liang Yan.OFDM sparse channel estimation based on adaptive compressed sensing［J］.Journal of Nanjing University of Posts and Telecommunications(Natural Science)，2019，39(2):49-54.)