收稿日期: 2016-01-21
修回日期: 2016-09-06
网络出版日期: 2017-03-30
基金资助
国家自然科学基金（No.61132003，No.61571282）；上海大学创新基金（No.sdcx20120041）资助
Multi-candidate Set of Generalized Orthogonal Matching Pursuit Algorithm
Received date: 2016-01-21
Revised date: 2016-09-06
Online published: 2017-03-30
田金鹏, 刘小娟, 刘燕平, 薛莹, 郑国莘 . 多候选集广义正交匹配追踪算法[J]. 应用科学学报, 2017 , 35(2) : 233 -243 . DOI: 10.3969/j.issn.0255-8297.2017.02.010
A multi-candidate set of generalized orthogonal matching pursuit algorithm is proposed to improve precision of greedy algorithms for compressed sensing. Multiple atoms are chosen as multiple candidates based on correlation of the inner product of observation matrix and residual. In the iteration, the multiple atoms are added to the multiple candidate sets, resulting in fast convergence of the algorithm. The candidate set with the smallest residuals is chosen as the final support set so that the sparse signal is exactly rebuild. Compared with other algorithms, experimental results show that the proposed algorithm has better recovery performance and lower recovery complexity.
[1] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306.
[2] Candes, E J, Romberg J, Tao T. Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2):489-509.
[3] Candes E J, Tao T. Near-optimal signal recovery from random projections:universal encoding strategies[J]. IEEE Transactions on Information Theory, 2006, 52(12):5406-5425.
[4] 丰祥,万旺根. 运用压缩感知理论的图像稀疏表示与重建[J]. 应用科学学报,2014, 32(5):447-452. Feng X, Wan W G. Sparse representation and reconstruction of image based on compressed sensing[J]. Journal of Applied Sciences, 2014, 32(5):447-452. (in Chinese)
[5] Mallat S, Zhang Z. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993, 41(12):3397-3415.
[6] Tropp J, Gilbert A. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12):4655-4666.
[7] 刘亚新,赵瑞珍,胡绍海,姜春晖. 用于压缩感知信号重建的正则化自适应匹配追踪算法[J]. 电子与信息学报,2010, 32(11):2713-2717. Liu Y X, Zhao R Z, Hu S H, Jiang C H. Regularized adaptive matching pursuit algorithm for signal reconstruction based on compressive sensing[J]. Journal of Electronics & Information Technology, 2010, 32(11):2713-2717. (in Chinese)
[8] 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.
[9] Donoho D L, Tsaig Y, Drori I, Drori I, Starck J L. Sparsity solution of underdetermined liner equations by stagewise orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2012, 58(2):1094-1121.
[10] Wang J, Kwon S, Shim B. Generalized orthogonal matching pursuit[J]. IEEE Transactions on Signal Processing, 2012, 60(12):6202-6216.
[11] Needell D, Tropp J A. CoSaMP:iterative signal recovery from incomplete and inaccurate samples. ACM Technical Report:2008-01[R]. Pasadena, USA:California Institute of Technology, 2008, 7.
[12] 杨成,冯巍,冯辉,杨涛,胡波. 一种压缩采样中的稀疏度自适应子空间追踪算法[J]. 电子学报,2010, 38(4):1914-1917. Yang C, Feng W, Feng H, Yang T, Hu B. A sparsity adaptive subspace pursuit algorithm for compressive sample[J]. Acta Sinica Electronica, 2010, 38(4):1914-1917. (in Chinese)
[13] Kwon S Y, Wang J, Shim B H. Multipath matching pursuit[J]. IEEE Transactions on Information Theory, 2014, 60(5):2986-3001.
[14] Baraniuk R. A lecture on compressive sensing[J]. IEEE Signal Processing Magazine, 2007, 24(4):118-121.
[15] Shen Y, Li B, Pan W L, Li J. Analysis of generalised orthogonal matching pursuit using restricted isometry constant[J]. Electronics Letters, 2014, 50(14):1020-1022.
[16] 杨真真,杨震,孙林慧. 信号压缩重构的正交匹配追踪类算法综述[J]. 信号处理,2013, 29(4):486-496. Yang Z Z, Yang Z, Sun L H. A survey on matching pursuit type algorithms for signal compression and reconstruction[J]. Signal Processing, 2013, 29(4):486-496. (in Chinese)
/
| 〈 |
|
〉 |
