Sparse Signal Reconstruction of Backtracking Generalized Orthogonal Matching Pursuit Algorithm Based on Secondary Screening

Abstract

Abstract: Compressed sensing is a new theory of signal sampling and reconstruction. Efficient signal reconstruction algorithm is the pivot of compressed sensing from theory to practical application. To reconstruct the original sparse signal more accurately，a backtracking generalized orthogonal matching pursuit algorithm based on secondary screening is proposed. Firstly, a large number of related atoms are selected to improve their utilization rate by using inner product matching criterion. Secondly, the generalized Jaccard coefficient criterion is used for the selected atoms to obtain the most matched atoms and optimize the atom selection method. The experimental results show that when the signal is reconstructed under different sparseness and observed values, the proposed algorithm has greater advantages in terms of reconstruction error and success rate compared with backtracking generalized orthogonal matching pursuit algorithm, orthogonal matching pursuit algorithm and subspace pursuit algorithm.

Key words: compressed sensing, backtracking generalized orthogonal matching pursuit algorithm, secondary screening, generalized Jaccard coefficient

ZHANG Lian-na, ZHANG Hui-ping, LI Rong-peng, SONG Xue-li. Sparse Signal Reconstruction of Backtracking Generalized Orthogonal Matching Pursuit Algorithm Based on Secondary Screening[J]. Computer and Modernization, 2022, 0(03): 111-115.

References ［24］

［1］	DONOHO D L. Compressed sensing［J］. IEEE Transactions on Information Theory, 2006,52(4):1289-1306.
［2］	DAI Y X， ZHUANG P X. Compressed sensing MRI via a multi-scale dilated residual convolution network［J］. Magnetic Resonance Imaging, 2019,63:93-104.
［3］	KOOCHAKZADEH A, MALEK-MOHAMMADI M, BABAIE-ZADEH M, et al. Multi-antenna assisted spectrum sensing in spatially correlated noise environments［J］. Signal Processing, 2015,108(3):69-76.
［4］	丁晖,赵海丞,刘家强,等. 基于压缩感知的电力设备状态感知技术［J］. 高电压技术, 2020,46(6):1877-1885.
［5］	LOPES M E. Unknown sparsity in compressed sensing: Denoising and inference［J］. IEEE Transactions on Information Theory, 2016,62(9):5145-5166.
［6］	CHEN W S, YOU J, CHEN B, et al. A sparse representation and dictionary learning based algorithm for image restoration in the presence of rician noise［J］. Neurocomputing, 2018,286:130-140.
［7］	JIE G, BIN S, YING H, et al. A survey on compressed sensing in vehicular infotainment systems［J］. IEEE Communications Surveys and Tutorials, 2017,19(4):2662-2680.
［8］	MOHIMANI H, BABAIE Z M, JUTTEN C. A fast approach for overcomplete sparse decomposition based on smoothed L0 norm［J］. IEEE Transactions on Signal Processing, 2009,57(1):289-301.
［9］	MALEK M M, BABAIE M, KOOCHAKZADEH A, et al. Successive concave sparsity approximation for compressed sensing［J］. IEEE Transactions on Signal Processing, 2016,64(21):5657-5671.
［10］	周洁容,李海洋,凌军,等. 基于非凸复合函数的稀疏信号恢复算法［J］. 自动化学报, 2020,48(1):1-12.
［11］	MOMENNEZHAD A. Matching pursuit algorithm for enhancing EEG signal quality and increasing the accuracy and efficiency of emotion recognition［J］. Biomedizinische Technik, 2020,65(4):393-404.
［12］	WEN J M, ZHOU Z C, WANG J, et al. A sharp condition for exact support recovery with orthogonal matching pursuit［J］. IEEE Transactions on Signal Processing, 2017,65(6):1370-1382.
［13］	ZHU H F, CHEN W, WU Y P. Efficient implementations for orthogonal matching pursuit［J］. Electronics, 2020,9(9):1507.
［14］	LU D X, SUN G L, LI Z Z, et al. Improved CoSaMP reconstruction algorithm based on residual update［J］. Journal of Computer and Communications, 2019,7(6):6-14.
［15］	HUANG F, TAO J, XIANG Y, et al. Parallel compressive sampling matching pursuit algorithm for compressed sensing signal reconstruction with OpenCL［J］. Journal of Systems Architecture, 2016,72(C):51-60.
［16］	KIM K S, CHUNG S Y. Greedy subspace pursuit for joint sparse recovery［J］. Journal of Computational and Applied Mathematics, 2016,352:308-327.
［17］	WANG J, KWON S, LI P, et al. Recovery of sparse signals via generalized orthogonal matching pursuit: A new analysis［J］. IEEE Transactions on Signal Processing, 2016,64(4):1076-1089.
［18］	ZHAO L Q, LIU Y L. A new generalized orthogonal matching pursuit method［J］. Journal of Electrical and Computer Engineering, 2017,2017(2):1-7.
［19］	LI G, LIU Q H, GU Y T. Rigorous restricted isometry property of low-dimensional subspaces［J］. Applied and Computational Harmonic Analysis, 2020,49(2):608-635.
［20］	SHEN Y, LI B, PAN W L, et al. Analysis of generalized orthogonal matching pursuit using restricted isometry constant［J］. Electronics Letters, 2014,50(14):1020-1022.
［21］	BARANIUK R G. Compressive sensing［J］. IEEE Signal processing magazine, 2007,24(4):118-121.
［22］	张晓东,董唯光,汤旻安,等. 压缩感知中基于广义Jaccard系数的GOMP重构算法［J］. 山东大学学报(理学版), 2017,52(11):23-28.
［23］	王坤,吴一鸣,诸葛晶昌,等. 一种基于广义Jaccard系数的MsGOMP红外图像去噪算法［J］. 红外技术, 2019,41(6):577-584.
［24］	张宇,刘雨东,计钊. 向量相似度测度方法［J］. 声学技术, 2009,28(4):532-536.

[1]	CHEN Xun-hao, YANG Ying, HUANG Jun-ru, SUN Yu-bao. Video Snapshot Compressed Sensing Reconstruction Based on Multi-scale Fusion Network [J]. Computer and Modernization, 2021, 0(12): 58-64.
[2]	JIN An-an, DING Shuang-shuang, XIONG Qing-zhi, XU Ting-ting, XI Shu-ping, MA Ji-zhong. Optimization Reconstruction of EPMA Image Based on SWOMP Algorithm [J]. Computer and Modernization, 2020, 0(12): 104-111.
[3]	ZHANG Lei, CUI Juan-juan, LI Jing-jing. A Clustering Data Collection Algorithm in WSN Based on Compressed Sensing [J]. Computer and Modernization, 2020, 0(09): 1-5.
[4]	ZHANG Huan1,2, LEI Hong1. Analysis of Phase Transition of Penalized Optimization Method in Corrupted Sensing Problem [J]. Computer and Modernization, 2019, 0(06): 76-.
[5]	YU Hong-xing1,2, SHEN Guo-wei1,2, GUO Chun1,2. Network Tunnel Detection Method Based on [J]. Computer and Modernization, 2019, 0(06): 1-.
[6]	WU Jia1, ZHAO Yun2, ZHANG Li-juan3, SONG Wen2. Prediction of Cellular Traffic Based on Space-time Compression Sensing [J]. Computer and Modernization, 2018, 0(12): 11-.
[7]	DONG Yuan-quan1, WANG Hao2. Multiple Target Localizationin WSNs via CS Reconstruction Method Based on Discrete CSO Algorithm [J]. Computer and Modernization, 2017, 0(12): 23-27.
[8]	SHEN Huijun. Efficient Compressed Sensing Magnetic Resonance Imaging #br# Based on Compound Regularization [J]. Computer and Modernization, 2016, 0(5): 39-45.
[9]	ZHONG Zhi-wei. Low-dose CT Image Reconstruction Based on Adaptive Kernel Regression Method and Algebraic Reconstruction Technique [J]. Computer and Modernization, 2016, 0(11): 38-42.
[10]	LIU Ji-cheng1, WANG Min-ying1, LI Hao-ran2. Image Reconstruction Based on Improved CoSaMP Algorithm [J]. Computer and Modernization, 2015, 0(5): 48-52.
[11]	LIU Hong-jiang;LI Wen-ying. Research on Technology of Quantum Tomography Based on Compressed Sensing [J]. Computer and Modernization, 2011, 12(12): 121-125.

Sparse Signal Reconstruction of Backtracking Generalized Orthogonal Matching Pursuit Algorithm Based on Secondary Screening

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References ［24］

Related Articles 11

Recommended Articles

Metrics

Comments