Block Sparse Signals Recovery Algorithm for Distributed Compressed Sensing Reconstruction
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초록
Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linearmeasurements. Various studies about DCS have been carried out recently. In many practical applications, thereis no prior information except for standard sparsity on signals. The typical example is the sparse signals haveblock-sparse structures whose non-zero coefficients occurring in clusters, while the cluster pattern is usuallyunavailable as the prior information. To discuss this issue, a new algorithm, called backtracking-based adaptiveorthogonal matching pursuit for block distributed compressed sensing (DCSBBAOMP), is proposed. Incontrast to existing block methods which consider the single-channel signal reconstruction, the DCSBBAOMPresorts to the multi-channel signals reconstruction. Moreover, this algorithm is an iterative approach, whichconsists of forward selection and backward removal stages in each iteration. An advantage of this method isthat perfect reconstruction performance can be achieved without prior information on the block-sparsitystructure. Numerical experiments are provided to illustrate the desirable performance of the proposed method.
Abstract
Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linearmeasurements. Various studies about DCS have been carried out recently. In many practical applications, thereis no prior information except for standard sparsity on signals. The typical example is the sparse signals haveblock-sparse structures whose non-zero coefficients occurring in clusters, while the cluster pattern is usuallyunavailable as the prior information. To discuss this issue, a new algorithm, called backtracking-based adaptiveorthogonal matching pursuit for block distributed compressed sensing (DCSBBAOMP), is proposed. Incontrast to existing block methods which consider the single-channel signal reconstruction, the DCSBBAOMPresorts to the multi-channel signals reconstruction. Moreover, this algorithm is an iterative approach, whichconsists of forward selection and backward removal stages in each iteration. An advantage of this method isthat perfect reconstruction performance can be achieved without prior information on the block-sparsitystructure. Numerical experiments are provided to illustrate the desirable performance of the proposed method.
참고문헌 (29)
- Q. Wang, "A robust and efficient algorithm for distributed compressed sensing", Computers &Electrical Engineering, 37(6), pp.916~926 (2011)
- H. Palangi, "Convolutional deep stacking networks for distributed compressive sensing", Signal Processing, 131, pp.181~189 (2017)
- Y. Oktar, "A review of sparsity-based clustering methods", Signal Processing, 148, pp.20~30 (2018)
- L. Vidya, "RBF network based sparse signal recovery algorithm for compressed sensing reconstruction", Neural Networks, 63, pp.66~78 (2015)
- X. Li, "A gradient-based approach to optimization of compressed sensing systems", Signal Processing, 139, pp.49~61 (2017)
- G. Coluccia, "Operational rate-distortion performance of single-source and distributed compressed sensing", IEEE Transactions on Communications, 62(6), pp.2022~2033 (2014)
- "Distributed compressed sensing" (2009)[URL]
- Y. J. Zhang, "Backtracking-based matching pursuit method for distributed compressed sensing", Multimedia Tools and Applications, 76(13), pp.14691~14710 (2017)
- Y. Zhang, "Forward-backward pursuit method for distributed compressed sensing", Multimedia Tools and Applications, 76(20), pp.20587~20608 (2017)
- Y. C. Eldar, "Average case analysis of multichannel sparse recovery using convex relaxation", IEEE Transactions on Information Theory, 56(1), pp.505~519 (2010)
- D. L. Donoho, "For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution", Communications on Pure and Applied Mathematics, 59(6), pp.797~829 (2006)
- M. F. Duarte, "Distributed compressed sensing of jointly sparse signals", Proceeding of the 39th Asilomar Conference on Signals, Systems and Computers, pp.1537~1541 (2005)
- M. B. Wakin, "Recovery of jointly sparse signals from few random projections", Advances in Neural Information Processing Systems, 18, pp.1435~1440 (2005)
- J. A. Tropp, "Simultaneous sparse approximation via greedy pursuit", Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (2005)
- D. Sundman, "Greedy pursuits for compressed sensing of jointly sparse signals", Proceedings of 2011 19th European Signal Processing Conference, pp.368~372 (2011)
- K. Lee, "Subspace methods for joint sparse recovery", IEEE Transactions on Information Theory, 58(6), pp.3613~3641 (2012)
- X. T. Yuan, "Visual classification with multitask joint sparse representation", IEEE Transactions on Image Processing, 21(10), pp.4349~4360 (2012)
- F. Parvaresh, "Recovering sparse signals using sparse measurement matrices in compressed DNA microarrays", IEEE Journal of Selected Topics in Signal Processing, 2(3), pp.275~285 (2008)
- S. F. Cotter, "Sparse channel estimation via matching pursuit with application to equalization", IEEE Transactions of Communications, 50(3), pp.374~377 (2002)
- R. Qi, "On recovery of block sparse signals via block generalized orthogonal matching pursuit", Signal Processing, 153, pp.34~46 (2018)
- R. G. Baraniuk, "Model-based compressive sensing", IEEE Transactions on Information Theory, 56(4), pp.1982~2001 (2010)
- D. Needell, "CoSaMP : iterative signal recovery from incomplete and inaccurate samples", Applied and Computational Harmonic Analysis, 26(3), pp.301~321 (2009)
- Y. C. Eldar, "Block-sparse signals : Uncertainty relations and efficient recovery", IEEE Transactions on Signal Processing, 58(6), pp.3042~3054 (2010)
- L. Zelnik-Manor, "Dictionary optimization for block-sparse representations", IEEE Transactions on Signal Processing, 60(5), pp.2386~2395 (2012)
- A. Kamali, "Block subspace pursuit for block-sparse signal reconstruction", Iranian Journal of Science and Technology: Transactions of Electrical Engineering, 37(E1), pp.1~16 (2013)
- B. X. Huang, "Recovery of block sparse signals by a block version of StOMP", Signal Processing, 106, pp.231~244 (2015)
- Rui Qi, "Block Sparse Signals Recovery via Block Backtracking- Based Matching Pursuit Method", JIPS(Journal of Information Processing Systems), 13(2), pp.360~369 (2017), 한국정보처리학회[DOI]
- E. Candes, "Robust uncertainty principles : exact signal reconstruction from highly incomplete frequency information", IEEE Transactions on Information Theory, 52(2), pp.489~509 (2006)
- A. L. Goldberger, "PhysioBank, PhysioToolkit, and PhysioNet : components of a new research resource for complex physiologic signals", Circulation, 101(23), pp.e215~e220 (2000)