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Open Access

A Reducing Iteration Orthogonal Matching Pursuit Algorithm for Compressive Sensing

Rui Wang( )Jinglei ZhangSuli RenQingjuan Li
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
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Abstract

In recent years, Compressed Sensing (CS) has been a hot research topic. It has a wide range of applications, such as image processing and speech signal processing owing to its characteristic of removing redundant information by reducing the sampling rate. The disadvantage of CS is that the number of iterations in a greedy algorithm such as Orthogonal Matching Pursuit (OMP) is fixed, thus limiting reconstruction precision. Therefore, in this study, we present a novel Reducing Iteration Orthogonal Matching Pursuit (RIOMP) algorithm that calculates the correlation of the residual value and measurement matrix to reduce the number of iterations. The conditions for successful signal reconstruction are derived on the basis of detailed mathematical analyses. When compared with the OMP algorithm, the RIOMP algorithm has a smaller reconstruction error. Moreover, the proposed algorithm can accurately reconstruct signals in a shorter running time.

References

[1]
Donoho D. L., Compressed sensing, IEEE Transactions on information Theory, vol. 52, no. 4, pp. 1289–1306, 2006.
[2]
Davenport M., Duarte M., Eldar Y. C., and Kutyniok G., Compressed Sensing: Theory and Applications. Cambridge, UK: Cambridge Univ. Press, 2011.
[3]
Bi H., Zhang B., and Hong W., Matrix completion-based distributed compressive sensing for polarimetric SAR tomography, SCIENCE CHINA Information Sciences, vol. 58, no. 11, pp. 1–3, 2015.
[4]
Hou Q., Liu Y., Fan L., and Su S., Compressed sensing digital receiver and orthogonal reconstructing algorithm for wideband ISAR radar, SCIENCE CHINA Information Sciences, vol. 58, no. 2, pp. 1–10, 2015.
[5]
Ning H., Liu H., and Yang L. T., Aggregated-proof based hierarchical authentication scheme for the internet of things, IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 3, pp. 657–667, 2014.
[6]
Bhattacharya S., Blumensath T., Mulgrew B., and Davies M., Fast encoding of synthetic aperture radar raw data using compressed sensing, in IEEE Workshop on Statistical Signal Processing, 2007, pp. 448–452.
DOI
[7]
Lustig M., Donoho D. L., and Pauly J. M., Rapid MR imaging with compressed sensing and randomly under sampled 3DFT trajectories, in Proceedings of the 14th. Annual Meeting of ISMRM, 2006.
[8]
Kirolos S., Laska J., Wakin M., and Duarte M., Analog to information conversion via random demodulation, in Proceedings of the IEEE Dallas Circuits and Systems Workshop, 2006, pp. 71–74.
DOI
[9]
Laska J., Kirolos S., and Massoud Y., Random sampling for analog to information conversion of wideband signals, in Proceedings of the IEEE Dallas Circuits and Systems Workshop, 2006, pp. 119–122.
DOI
[10]
Borgnat P. and Flandrin P., Time frequency localization from sparsity constraints, in IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 2008.
DOI
[11]
Dai W. and Milenkovic O., Subspace pursuit for compressive sensing signal reconstruction, IEEE Trans. Inf. Theory, vol. 55, no. 5, pp. 2230–2249, 2008.
[12]
Stuber C., Kuppinger P., and Pope G., Recovery of sparsely corrupted signals, IEEE Trans. Inf. Theory, vol. 58, no. 5, pp. 3115–3130, 2012.
[13]
Wu R., Huang W., and Chen D. R., The exact support recovery of sparse signals with noise via orthogonal matching pursuit, IEEE Signal Process Lett., vol. 20, no. 4, pp. 403–406, 2013.
[14]
Kong L. and Xia M., Data loss and reconstruction in wireless sensor networks, IEEE Transaction on Parallel and Distributed Systems, vol. 25, no. 11, pp. 2818–2828, 2014.
[15]
Needell D. and Tropp J. A., CoSaMP: Iterative signal recovery from incomplete and inaccurate samples, Appl. Comp. Harmonic Anal., vol. 26, no. 12, pp. 93–100, 2008.
[16]
Chen S. S., Donoho D. L., and Saunders M. A., Atomic decomposition by basis pursuit, SIAM J. Sci. Comput., vol. 58, no. 1, pp. 33–61, 1999.
[17]
Tropp J. and Gilbert A., Signal recovery from random measurements via orthogonal matching pursuit, IEEE Transactions on Information Theory, vol. 53, no. 12, pp. 4655–4666, 2007.
[18]
Freedman D., Pisani R., and Purves R., Statistics. Norton & Company, 1998.
Tsinghua Science and Technology
Pages 71-79
Cite this article:
Wang R, Zhang J, Ren S, et al. A Reducing Iteration Orthogonal Matching Pursuit Algorithm for Compressive Sensing. Tsinghua Science and Technology, 2016, 21(1): 71-79. https://doi.org/10.1109/TST.2016.7399284

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Received: 12 September 2015
Revised: 13 October 2015
Accepted: 02 November 2015
Published: 04 February 2016
© The author(s) 2016
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