基于选择性测量的压缩感知去噪重构算法

doi:10.11959/j.issn.1000-436x.2017033

Abstract

Abstract:

In order to reduce the effect of noise folding (NF) phenomenon on the performance of sparse signal recon-struction,a new denoising recovery algorithm based on selective measure was proposed.Firstly,the NF phenomenon in compressive sensing (CS) was explained in theory.Secondly,a new statistic based on compressive measurement data was proposed,and its probability density function (PDF) was deduced and analyzed.Then a noise filter matrix was constructed based on the PDF to guide the optimization of measurement matrix.The optimized measurement matrix can selectively sense the sparse signal and suppress the noise to improve the SNR of the measurement data,resulting in the improvement of sparse reconstruction performance.Finally,it was pointed out that increasing the measurement times can further enhance the performance of denoising reconstruction.Simulation results show that the proposed denoising recon-struction algorithm has a better improvement in the performance of reconstruction of noisy signal,especially under low SNR.

Key words: compressive sensing, signal reconstruction, noise folding, selective measure

CLC Number:

TN911.72

Li-ye PEI,Hua JIANG,Yue-liang MA. Denoising recovery for compressive sensing based on selective measure[J]. Journal on Communications, 2017, 38(2): 106-114.

Figures/Tables 4

References 22

[3]	DONOHO D L . Compressed sensing[J]. IEEE Trans on Information Theory, 2006,52(4): 1289-1306.
[4]	焦李成, 杨淑媛, 刘芳 ,等. 压缩感知回顾与展望[J]. 电子学报, 2011,39(7): 1561-1662.
	JIAO L C , YANG S Y , LIU F ,et al. Development and prospect of compressive sensing[J]. Acta Electronica Sinica, 2011,39(7): 1561-1662.
[5]	王韦刚, 杨震, 顾彬 . 基于观测矩阵优化的自适应压缩频谱感知[J]. 通信学报, 2014,35(8): 33-39.
	WANG W G , YANG Z , GU B . Adaptive compressed spectrum sensing based on optimized measurement matrix[J]. Journal on Communications, 2014,35(8): 33-39.
[6]	田文飚, 付争, 芮国胜 . 基于分治试探的盲自适应匹配追踪重构算法[J]. 通信学报, 2013,34(4): 180-186.
	TIAN W B , FU Z , RUI G S . Blind adaptive matching pursuit algorithm for signal reconstruction based on sparsity trial and error[J]. Journal on Communications, 2013,34(4): 180-186.
[7]	JIN S , ZHANG X . An efficient method for collaborative compressive spectrum sensing in cognitive radio networks[C]// IEEE Global Communications Conference. 2014: 834-839.
[8]	ZHAO H H , WANG Y N , PENG X J ,et al. Gradientbased compressive sensing for noise image and video reconstruction[J]. IET Communications, 2015,9(7): 940-946.
[9]	KRAHMER F , WARD R . Stable and robust sampling strategies for compressive imaging[J]. IEEE Transactions on Image Processing, 2014,23(2): 612-622.
[10]	DONOHO D L , TSAIG Y . Extensions of compressed sensing[J]. Signal Processing, 2006,86(3): 533-548.
[11]	CANDES E J , TAO T . The dantzig selector:statistical estimation when p is much larger than n[J]. Annals of Statistics, 2007,35(6): 2313-2353.
[12]	NEEDELL D , VERSHYNIN R . Signal recovery from inaccurate and incomplete measurements via regularized orthogonal matching pursuit[J]. IEEE Journal of Selected Topics in Signal Processing, 2010,4(2): 310-316.
[13]	DAI W , MILENKOVIC O . Subspace pursuit for compressive sensing signal reconstruction[J]. IEEE Transactions on Information Theory, 2009,55(5): 2230-2249.
[14]	NEEDELL D , TROPP J A . CoSaMP:iterative signal recovery from incomplete and inaccurate samples[J]. Applied and Computational Harmonic Analysis, 2008,26(3): 301-321.
[15]	CASTRO E S , ELDAR Y C . Noise folding in compressed sensing[J]. IEEE Signal Processing Letters, 2011,18(8): 478-481.
[16]	TREICHLER J , DAVENPORT M A , BARANIUK R G . Application of compressive sensing to the design of wideband signal acquisition receivers[C]// In 6th U.S. /Australia Joint Workshop on Defense Applications of Signal Processing, 2009.
[17]	ALMEROTH B , KRONE S , FETTWEIS G . Analyzing the signal to noise ratio of direct sampling receivers[C]// IEEE International Conference on Communications. Budapest, 2013: 4561-4565.
[1]	SUN H , NALLANATHAN A , WANG C X ,et al. Wideband spectrum sensing for cognitive radio networks :a survey[J]. IEEE Wireless Communications, 2013,20(2): 74-81.
[2]	CANDèS E J , WAKIN M N . An introduction to compressive sampling[J]. IEEE Signal Processing Magazine, 2008,25(2): 21-30.
[18]	PETER S , ARTINA M , FORNASIER M . Damping noise folding and enhanced support recovery in compressed sensing[J]. IEEE Transactions on Signal Processing, 2015,63(22): 5990-6002.
[19]	HAUPT J , CASTRO R , NOWAK R . Distilled sensing:adaptive sampling for sparse detection and estimation[J]. IEEE Transactions on Information Theory, 2011,57(9): 6222-6235.
[20]	HAUPT J , BARANIUK R , CASTRO R ,et al. Compressive distilled sensing:sparse recovery using adaptivity in compressive measurements[C]// In Proceedings of the 43rd Asilomar Conference on Signals,Systems and Computers. 2009: 1551-1555.
[21]	TROPP J , GILBERT A . Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007,53(12): 4655-4666.
[22]	WANG Y , TIAN Z , FENG C Y . Sparsity order estimation and its application in compressive spectrum sensing for cognitive radios[J]. IEEE Transactions on Wireless Communications, 2012,11(6): 2116-2125.

Metrics

Recommended 0

No Suggested Reading articles found!

Denoising recovery for compressive sensing based on selective measure

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 4

References 22

Related Articles 15

Metrics

Recommended 0

[1]	Ang LI, Jianxin CHEN, Xin WEI, Liang ZHOU. 6G-oriented cross-modal signal reconstruction technology [J]. Journal on Communications, 2022, 43(6): 28-40.
[2]	Hua REN, Shaozhang NIU, Ruyong REN, Zhen YUE. Research on meaningful image encryption algorithm based on 2-dimensional compressive sensing [J]. Journal on Communications, 2022, 43(5): 45-57.
[3]	Zuozhou PAN,Zong MENG,Jing LI,Ying SHI. Compressed sensing reconstruction algorithm based on adaptive acceleration forward-backward pursuit [J]. Journal on Communications, 2020, 41(1): 25-32.
[4]	Weina YUAN,Qiu YAN. Channel estimation method based on compressive sensing for FBMC/OQAM system [J]. Journal on Communications, 2019, 40(12): 98-104.
[5]	Feiyun WU,Kunde YANG,Feng TONG. Partial-norm-constrained sparse recovery algorithm and its application on single carrier underwater-acoustic-data telemetry [J]. Journal on Communications, 2018, 39(6): 127-132.
[6]	Ce ZHANG,Ou LI,Xin TONG,Yanping YANG. WSN data gathering algorithm based on compressive sensing and matrix completion technique [J]. Journal on Communications, 2018, 39(2): 164-173.
[7]	Xin PENG,Ren-fa LI,Zhe-tao LI,Guo-qi XIE. Data dissemination of VANET based on lower bound of road delay [J]. Journal on Communications, 2017, 38(4): 25-34.
[8]	Peng SUN,Gui-nan LI,Lian-tao WU,Zhi WANG. Sparse signal transmission under lossy wireless links based on double process of compressive sensing [J]. Journal on Communications, 2017, 38(4): 120-128.
[9]	Jia LI,Zhi-rong GAO,Cheng-yi XIONG,Cheng ZHOU. Image compressive sensing recovery based on weighted structure group sparse representation [J]. Journal on Communications, 2017, 38(2): 196-202.
[10]	Li-jun GE,Hui GUO,Yue LI,Lan ZHAO. Sparsity adaptive channel estimation algorithm based on compressive sensing for massive MIMO systems [J]. Journal on Communications, 2017, 38(12): 57-62.
[11]	Yi-yin LIU,Guo-rui LI,Li TIAN. Joint sparse model based data reconstruction algorithm for wireless sensor network [J]. Journal on Communications, 2016, 37(Z1): 211-218.
[12]	Ce ZHANG,Xia ZHANG,Ou LI,Guan-lin MEI,Zhe HAN,Da-long ZHANG,Guang-yi LIU. Compressive sensing based data gathering algorithm over unreliable links in WSN [J]. Journal on Communications, 2016, 37(9): 131-141.
[13]	Lei LIU,Jian-jun ZHANG,Yang LU,Xing WEI,Jiang-hong HAN. Multiple targets localization via compressive sensing from mere connectivity [J]. Journal on Communications, 2016, 37(5): 152-164.
[14]	HOUWei Z,INGBo J,UANGYi-feng H,IAOXiao-xuan J,UJia-xing H,IANGWei L. CS-based data collection method for airborne clustering WSN [J]. Journal on Communications, 2015, 36(5): 130-139.
[15]	Lin-shu HUANG,Hao CHA,Hui-juan YE,Kai XU. Analysis on the DOA estimation of uniform circular arrays based on compressive sensing [J]. Journal on Communications, 2015, 36(2): 168-174.