Orthogonal Matching Pursuit based recovery for correlated sources with partially disjoint supports
作者： Inaki Esnaola , Rafael E. Carrillo , Javier Garcia-Frias , Kenneth E. Barner
DOI: 10.1109/CISS.2010.5464901
关键词:
摘要: Compressed sensing (CS) can be applied in distributed scenarios, where the objective is to independently compress several signals that are characterized by presenting a sparse correlation. In this case, compressed version of each signal produced without knowledge other signals. The decoder has access versions all interest and recovers them exploiting correlations. Motivated idea incorporating prior information CS we propose study effects including support correlation reconstruction process. We investigate performance improvement obtained jointly recovering two correlated sources, compared single source recovery, terms number samples (measurements) required encode for successful recovery. To perform modify OMP algorithm recover sources with partially disjoint support. final an iterative process incorporates resembling joint channel digital coding schemes, probabilistic iteratively exchanged. carried out means numerical simulations synthetic model.
sci-hub.se 参考文章(17)
Dror Baron, Marco F. Duarte, Michael B. Wakin, Shriram Sarvotham, Richard G. Baraniuk, Distributed Compressive Sensing arXiv: Information Theory. ,(2009) , 10.21236/ADA521228
Joel A. Tropp, Anna C. Gilbert, Martin J. Strauss, Algorithms for simultaneous sparse approximation: part I: Greedy pursuit Signal Processing. ,vol. 86, pp. 572- 588 ,(2006) , 10.1016/J.SIGPRO.2005.05.030
Rick Chartrand, Valentina Staneva, Restricted isometry properties and nonconvex compressive sensing Inverse Problems. ,vol. 24, pp. 035020- ,(2007) , 10.1088/0266-5611/24/3/035020
Marco F. Duarte, Chinmay Hegde, Volkan Cevher, Richard G. Baraniuk, Recovery of compressible signals in unions of subspaces conference on information sciences and systems. pp. 175- 180 ,(2009) , 10.1109/CISS.2009.5054712
Shihao Ji, Ya Xue, Lawrence Carin, Bayesian Compressive Sensing IEEE Transactions on Signal Processing. ,vol. 56, pp. 2346- 2356 ,(2008) , 10.1109/TSP.2007.914345
Javier Garcia-Frias, Inaki Esnaola, Exploiting Prior Knowledge in The Recovery of Signals from Noisy Random Projections data compression conference. pp. 333- 342 ,(2007) , 10.1109/DCC.2007.37
E.J. Candes, M.B. Wakin, An Introduction To Compressive Sampling IEEE Signal Processing Magazine. ,vol. 25, pp. 21- 30 ,(2008) , 10.1109/MSP.2007.914731
Joel A. Tropp, Anna C. Gilbert, Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit IEEE Transactions on Information Theory. ,vol. 53, pp. 4655- 4666 ,(2007) , 10.1109/TIT.2007.909108
E.J. Candes, T. Tao, Decoding by linear programming IEEE Transactions on Information Theory. ,vol. 51, pp. 4203- 4215 ,(2005) , 10.1109/TIT.2005.858979
I Esnaola, Javier Garcia-Frias, Distributed Compression of Correlated Signals Using Random Projections data compression conference. pp. 322- 331 ,(2008) , 10.1109/DCC.2008.60