Given a large sparse signal, great wishes are to

\r\nreconstruct the signal precisely and accurately from lease number of

\r\nmeasurements as possible as it could. Although this seems possible

\r\nby theory, the difficulty is in built an algorithm to perform the

\r\naccuracy and efficiency of reconstructing. This paper proposes a new

\r\nproved method to reconstruct sparse signal depend on using new

\r\nmethod called Least Support Matching Pursuit (LS-OMP) merge it

\r\nwith the theory of Partial Knowing Support (PSK) given new method

\r\ncalled Partially Knowing of Least Support Orthogonal Matching

\r\nPursuit (PKLS-OMP).

\r\nThe new methods depend on the greedy algorithm to compute the

\r\nsupport which depends on the number of iterations. So to make it

\r\nfaster, the PKLS-OMP adds the idea of partial knowing support of its

\r\nalgorithm. It shows the efficiency, simplicity, and accuracy to get

\r\nback the original signal if the sampling matrix satisfies the Restricted

\r\nIsometry Property (RIP).

\r\nSimulation results also show that it outperforms many algorithms

\r\nespecially for compressible signals.<\/p>\r\n","references":"[1] Wei Dai and Olgica Milenkovic, \u201cSubspace Pursuit for Compressive\r\nSensing: Closing the Gap Between Performance and Complexity\u201d,\r\nInternational Journal of Electronics and Computer Science Engineering\r\n2010.\r\n[2] Entao Liu and V.N. \u201cOrthogonal Super Greedy Algorithm and\r\nApplications in Compressed Sensing\u201d, IEEE Trans. Inform. Theory 58\r\n(4), 2040-2047,2011\r\n[3] D. Needel and J. A. Tropp\u201cCOSAMP: Iterative Signal Recovery from\r\nIncomplete and Inaccurate Samples\u201d, Elsevier applied and\r\nComputational Harmonic. Anal. 26 (2009) 301\u2013321 Volume 26, Issue 3,\r\n[4] Wei Dai, and Olgica Milenkovic , \u201cSubspace Pursuit for Compressive\r\nSensing Signal Reconstruction\u201d, IEEE, 5, May (2009).\r\n[5] Parichat Sermwuthisarn, Duangrat Gansawat, \u201cImpulsive noise rejection\r\nmethod for compressed measurement signal in compressed sensing\u201d,\r\nEURASIP Journal on advances in signal processing 2012.\r\n[6] Tony TonyCai, Lie Wang, and GuangwuXu, \u201c Stable Recovery of\r\nSparse Signals and an Oracle Inequality \u201c, IEEE Transactions On\r\nInformation Theory, Vol. 56, No. 7, July 2010.\r\n[7] Jian Wang and ByonghyoShim , \u201cA Simple Proof of the Mutual\r\nIncoherence Condition for Orthogonal Matching Pursuit\u201d, arXiv\r\n1105.4408v1 (cs.It), 23 May (2011).\r\n[8] Richard Baraniuk, \u201cCompressive sensing\u201d, IEEE Signal Processing\r\nMagazine, 24(4), pp. 118-121, July 2007.\r\n[9] Joel A. Tropp, and Anna C. Gilber, \u201cSignal Recovery From Random\r\nMeasurements Via Orthogonal Matching Pursuit\u201d, IEEE Transactions on\r\nTheory, vol. 53, NO. 12, DECEMBER 2007\r\n[10] Parichat Sermwuthisarn, Supatana Auethavekiat, \u201dRobust reconstruction\r\nalgorithm for compressed sensing in Gaussian noise environment using\r\northogonal matching pursuit with partially known support and random\r\nsub sampling\u201d, EURASIP Journal on Advances in Signal Processing\r\n2012\/1\/3\r\n[11] Parichat Sermwuthisarn, Supatana Auethavekiat, \u201cImpulsive noise\r\nrejection method for compressed measurement signal in compressed\r\nsensing \u201d,EURASIP Journal on Advances in Signal Processing , 2012:68\r\n[12] Jian Wang and Byonghyo Shim , \u201cA Simple Proof of the Mutual\r\nIncoherence Condition for Orthogonal Matching Pursuit\u201d, arXiv\r\n1105.4408v1 (cs.It), 23 May (2011).","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 94, 2014"}