原子集校正及步长可控的稀疏度未知CS 重构

doi:10.3969/j.issn.0255-8297.2014.02.008

Abstract

Abstract: This paper models residual signals with Gaussian-like distributions, based on which consistency between the Backtracking-based adaptive orthogonal matching pursuit (BAOMP) threshold and signal sparselevel is analyzed. An improved BAOMP (IBAOMP) method is thenproposed. Themethod estimates the
preliminary matching state usingthe 80-20 rule, and introduces a threshold with variable step size to subtly adjust atom set to raise the correct rate of selected atoms and avoid quasi-periodic mismatches of residual signals. Simulation results of 500 tests show that the exact recovery probability of IBAOMP is 17%-26% higher than BAOMP for Gaussian sparse signals, and more than70% higher than BAOMP for natural images.

Key words: compressive sensing (CS), backtracking orthogonal matching pursuit (OMP), matching set calibration, incremental step control

CLC Number:

TN911.7

ZENG Chun-yan1,2, MA Li-hong1, DU Ming-hui1. Atom Set Calibration and Step Control for Unknown-Sparsity Reconstruction from Compressive Sensing[J]. Journal of Applied Sciences, 2014, 32(2): 163-169.

References

[1] GIRYES R, ELAD M. RIP-Based Near-Oracle Performance Guarantees for SP, CoSaMP, and IHT [J]. IEEE Transactions on Signal Processing, 2012, 60(3): 1465-1468.

[2] YANG Jing Yu, PENG Yi Gang, XU Wen Li, DAI Qiong Hai. Ways to sparse representation: a comparative study[J]. Tsinghua Science and Technology, 2009, 14(4): 434-443.

[3] TROPP J A, WRIGHT S J. Computational methods for sparse solution of linear inverse problems[J]. Proceedings of the IEEE, 2010, 98(6): 948-958.

[4] MALLAT S G, ZHANg Zhi Feng. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3397–3415.

[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] VARADARAJAN B, KHUDANPUR S, TRAN T D. Stepwise optimal subspace pursuit for improving sparse recovery[J]. IEEE Signal Processing Letters, 2011, 18(1): 27-30.

[7] NEEDELL D, TROPP J A. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples[J]. Applied and Computational Harmonic Analysis, 2009, 26(3): 301-321.

[8] BLUMENSATH T, DAVIES M E. Iterative hard thresholding for compressed sensing[J]. Applied and Computational Harmonic Analysis, 2009, 27(3): 265-274.

[9] HUANG Hong Lin, MAKUR Anamitra. Backtracking-Based Matching Pursuit Method for Sparse Signal Reconstruction[J]. IEEE Signal Processing Letters, 2011, 18(7): 391-394.

[10] GURBUZ A C, PILANCI M, ARIKAN O. Expectation maximization based matching pursuit[C]// IEEE International Conference on Acoustics, Speech and Signal Processing, 2012:3313-3316.

[11] DAI Wei, MILENKOVIC Olgica. Subspace pursuit for compressive sensing signal reconstruction[J]. IEEE Transactions on Information Theory, 2009, 55(5): 2230-2249.

[1]	SHEN Xian-li, XIE Wei, XU You-yun, XU Kui, ZHANG Dong-mei. Joint Subcarrier Suppression and Relay Selection for Two-Way OFDM Relaying System with PLNC [J]. Journal of Applied Sciences, 2016, 34(1): 30-41.
[2]	. Source Localization Using Taylor Series Iteration with Perturbation Calibration of Emitter States [J]. Journal of Applied Sciences, 2015, 33(3): 274-289.
[3]	WANG Yong, LI Hong, QI Li-na. Multi-task Compressed Spectrum Sensing Based on Belief Propagation Grouping [J]. Journal of Applied Sciences, 2014, 32(4): 341-348.
[4]	WANG Ri-ming1,2, FENG Jiu-chao1. Improved Localization in Dynamic Non-line-of-Sight Environments Using a Modified Log Path-Loss Model [J]. Journal of Applied Sciences, 2014, 32(4): 372-378.
[5]	HUANG Li-xun, FANG Yong. Impact of Random Delays in Control and Measurement Signals on Iterative Learning Control Systems [J]. Journal of Applied Sciences, 2014, 32(2): 156-162.
[6]	YU Fu-ping1,2, FENG You-qian1, LEI Teng1, LI Zhe1. Effects of Forgetting Factor on RLS Dictionary Learning [J]. Journal of Applied Sciences, 2014, 32(1): 44-50.
[7]	SUN Zhong-wei, ZHANG Xiao-fei, WU Hai-lang, LI Jian-feng. Multi-dimensional Angle Estimation in Bistatic MIMO Radar for L-Shaped Array with Propagator Method [J]. Journal of Applied Sciences, 2014, 32(1): 57-64.
[8]	. Modulation Classification Using Cyclostationarity Test and Support Vector Machine [J]. Journal of Applied Sciences, 2013, 31(6): 593-600.
[9]	PAN Jie, ZHOU Jian-jiang. Khatri-Rao Subspace Wideband DOA Estimation for Nonlinear Array [J]. Journal of Applied Sciences, 2013, 31(2): 159-164.
[10]	FANG Yong1,2, ZHAO Wei-jie1,2, WANG Min1,2. Estimation of Fast Fading Channel for OFDM Systems Using Compressed Channel Expression [J]. Journal of Applied Sciences, 2012, 30(6): 581-587.
[11]	SHI Lei1, HAN Jiang-hong1, SHI Yi2, WEI Zhen-chun1. Cross-Layer Optimization for Wireless Mesh Network with Multi-packet Reception [J]. Journal of Applied Sciences, 2012, 30(3): 227-233.
[12]	LIU Fu-gang1;2, DIAO Ming1. DOA Estimation of Wideband Signals Based on Cyclostationarity [J]. Journal of Applied Sciences, 2012, 30(2): 146-150.
[13]	CUI Wei-liang, JIANG Hua, LI Jian-qiang. Cyclostationarity Analysis of ZPOFDM Signals and Its Application to Classification of Multicarrier Modulation Signals [J]. Journal of Applied Sciences, 2012, 30(2): 128-134.
[14]	YUAN Xue, SONG Yong-duan. Human Tracking Algorithm Robust against Sudden Changes of Illumination [J]. Journal of Applied Sciences, 2011, 29(6): 592-597.
[15]	ZHAO Chun-hui, YANG Wei-chao, DU Yu. Modulation Recognition Using Fractional Low-Order Cyclic Spectrum Coherence Coefficient [J]. Journal of Applied Sciences, 2011, 29(6): 565-570.

Atom Set Calibration and Step Control for Unknown-Sparsity Reconstruction from Compressive Sensing

PDF

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments