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电子学报  2013 

脉冲噪声环境下高斯稀疏信源贝叶斯压缩感知重构

DOI: 10.3969/j.issn.0372-2112.2013.02.025, PP. 363-370

Keywords: 脉冲噪声,压缩感知,贝叶斯理论,鲁棒统计学

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Abstract:

大多数现有的压缩感知重构算法对脉冲噪声不具有鲁棒性,在脉冲噪声环境下,重构性能急剧下降,使得整个重构系统崩溃.针对此问题,本文提出了一种脉冲噪声环境下的稀疏重构算法BINSR算法,其基于贝叶斯理论,可以有效地估计出信号的支撑集和脉冲噪声中脉冲的位置,并且根据压缩感知观测序列的democracy特性,利用最小均方误差MMSE估计量,有效地估计出原信号.在此基础上,本文结合鲁棒统计学,提出自适应的ABINSR算法,使其不再依赖于信号以及噪声的统计参数.实验结果表明,BINSR算法在脉冲噪声环境下可以有效地恢复出稀疏信号,很大程度上改善了脉冲噪声环境下算法的重构性能.ABINSR算法不仅对脉冲噪声具有鲁棒性,而且可以在高斯白噪声环境下实现有效的信号重构.

References

[1]  Donoho D L.Compressed sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[2]  Tasig Y,Donoho D L.Extensions of compressed sensing[J].Signal Processing,2006,86(3):533-548.
[3]  Dai W,Milenkovic O.Subspace pursuit for compressive sensing[J].IEEE Transactions on Information Theory,2009,55(5):2230-2249.
[4]  Ji S H,Xue Y,Carin L.Bayesian compressive sensing[J].IEEE Transactions on Signal Processing,2008,56(6):2346-2356.
[5]  Schniter P,Potter L C,Ziniel J.Fast Bayesian matching pursuit .Information Theory and Applications Workshop-Conference Proceedings .San Diego,CA,USA,2008.326-333.
[6]  Maronna R A,Martin R D,Yohai V J.Robust Statistics:Theory and Methods[M].New York:John Wiely & Sons,2006.
[7]  X Wang,H V Poor.Robust Multiuser detection in Non-gaussian channels[J].IEEE Transactions on Signal Processing,1999,86(3):549-571.
[8]  S V Zhidkov.Analysis and comparison of several simple impulsive noise mitigation schemes for OFDM receivers[J].IEEE Transactions on Communications,2008,56(1):5-9.
[9]  R Chan,C Ho,M Nikolova.Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization[J].IEEE Transactions on Image Processing,2005,14(10):1479-1485.
[10]  C Studer,P Kuppinger,G Pope,H Bolcskei.Sparse signal recovery from sparsely corrupted measurements[A].IEEE International Symposium on Information Theory-Proceedings[C].St Petersburg,Russia,2011.1422-1426.
[11]  Cohen A,Dahmen W,Devore R.Compressed sensing and best k-term approximation[J].Journal of American Society,2009,27(3):265-274.
[12]  Figueiredo Mario A T,Nowak R D,Wright S J.Gradient projection for sparse reconstruction:application to compressed sensing and other inverse problems[J].IEEE Journal on Selected Topics in Signal Processing,2007,1(4):586-597.
[13]  Donoho D L,Tsaig Y,Strack J L.Sparse solution of underdetermined linear equation by stagewise orthogonal matching pursuit[J].IEEE Transactions on Information Theory,2012,58(2):1094-1121.
[14]  Do T T,Gan L,Nguyen N,Tran T D.Sparsity adaptive matching pursuit algorithm for practical compressed sensing .42nd Asilomar Conference on Signals,Systems and Computers .Pacific Grove,CA,USA,2008.581-587.
[15]  Candes E J,Tao T.Decoding by linear programming[J].IEEE Transactions on Information Theory,2005,51(2):4203-4215.
[16]  孙林慧,杨震,叶蕾.基于自适应多尺度压缩感知的语音压缩与重构[J].电子学报,2011,39(1):40-45. Sun Linhui,Yang Zhen,Ye Lei.Speech compression and reconstruction based on adaptive multiscale compression sensing theory[J].Acta Electronica Sinica,2011,39(1):40-45.(in Chinese)
[17]  叶蕾,杨震,王天荆,孙林慧.行阶梯观测矩阵、对偶仿射尺度内点重构算法下的语音压缩感知[J].电子学报,2012,40(3):429-434. Ye Lei,Yang Zhen,Wang Tianjing,Sun Linhui.Compressed sensing of speech signal based on row echelon measurement matrix and dual affine scaling interior point reconstruction method[J].Acta Electronica Sinica,2012,40(3):429-434.(in Chinese)
[18]  Tibshirani R.Regression shrinkage and selection via the lasso[J].Journal Royal Statistical Society B,1996,58:267-288.
[19]  Laska J N,Davenport M A,Baraniuk R G.Exact signal recovery from sparsely corrupted measurements through the pursuit of justice[A].43rdAsilomar Conference on Signals,Systems and Computers[C].Pacific Grove,CA,USA,2009.1556-1560.
[20]  L Lampe.Bursty impulse noise detection by compressed sensing [A].IEEE International Symposium on Powerline Communications and Its Applications[C].Udine,Italy,2011.29-34.
[21]  G Arce.Nonlinear Signal Process:A Statistical Approach[M].New York:Wiley,2005.
[22]  R E Carrillo,K E Barner,T C Aysal.Robust sampling and reconstruction methods for sparse signals in the presence of impulsive noise[J].IEEE Journal of Selected Topics in Signal Processing,2010,4(2):392-408.
[23]  Baraniuk R,Devenport M,Devore R,Wakin M.A simple proof of the restricted isometry property for random matrices[A].Compute Rendus de I''Academie des Science[C].Paris,2008.589-592.
[24]  Chen S S,Donoho D L,Saunders M A.Atomic decomposition by basis pursuit[J].SIAM Review,2001,43(1):129-159.
[25]  Tropp J,Gilbert A.Signal recovery from random measurements via orthogonal matching pursuit[J].IEEE Transactions on Information Theory,2007,53(12):4655-4666.
[26]  Needell D,Vershynin R.Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit[J].IEEE Journal on Selected Topics in Signal Processing,2010,4(2):310-316.
[27]  Qiu K,Aleksandar D.Variance-component based sparse signal reconstruction and model selection[J].IEEE Transactions on Signal Processing,2010,58(6):2935-2952.
[28]  Pham D S,Venkatesh S.Improved image recovery from compressed data contaminated with impulsive noise[J].IEEE Transactions on Image Processing,2012,21(1):397-405.
[29]  Davenport M A,Laska J N,Boufounos P T.A simple proof that random matrices are democratic[J].Const Approximation,2008,28(3):253-263.
[30]  Laska J N,Boufounos P T,Davenport P T,Baraniuk R G.Democracy in action:quantization,saturation and compressive sensing[J].Applied and Computational Harmonic analysis,2011,31(3):429-443.
[31]  D.Gervini,V J Yohai.A class of robust and fully efficient regression estimators[J].The Annals of Statistics,2002,30(2):583-616.

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