International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 4, No. 12, 8 Nov. 2012
Cover page and Table of Contents: PDF (size: 1672KB)
Author(s)
Khalid SABRI
1,*
Index Terms
Cyclosparsity, Sparsity, Cyclostationary, Deconvolution, Greedy
Abstract
The purpose of this study is to introduce the concept of cyclic sparsity or cyclosparsity in deconvolution framework for signals that are jointly sparse and cyclostationary. Indeed, all related works in this area exploit only one property, either sparsity or cyclostationarity and never both properties together. Although, the key feature of the cyclosparsity concept is that it gathers both properties to better characterize this kind of signals. We show that deconvolution based on cyclic sparsity increases the performances and reduces significantly the computation cost. Finally, we use simulations to investigate the behavior in deconvolution framework of the algorithms MP, OMP and theirs respective extensions to cyclic sparsity context, Cyclo-MP and Cyclo-OMP.
Cite This Paper
Khalid SABRI,"Cyclic Sparse Greedy Deconvolution", IJIGSP, vol.4, no.12, pp.1-8, 2012. DOI: 10.5815/ijigsp.2012.12.01
Reference
[1]C. Andrieu, P. Duvaut, and A. Doucet, “Bayesian deconvolution of cyclostationary processes based on point processes,” in EUSIPCO, Trieste, Italy, 10-13 Sep 1996.
[2]D. Hatzinakos J. Ilow, “Recursive least squares algorithm for blind deconvolution of channels with cyclostationary inputs,” in IEEE Military Communications Conference, Boston, Massachusetts, 1993, pp. 123–126.
[3]D. Hatzinakos, “IEEE trans. on sp,” Nonminimum phase channel deconvolution using the complex cepstrum of the cyclic autocorrelation, vol. 42, no. 11, pp. 3026–3042, Nov. 1994.
[4]L. Cerrato and B. Eisenstein, “IEEE trans. on acoustics, speech, and sp,” Deconvolution of cyclostationary signals, vol. 25, no. 6, pp. 466–476, Dec. 1977.
[5]K.M. Cheung and S.F. Yau, “Blind deconvolution of system with unknown response excited by cyclostationary impulses,” in IEEE, ICASSP, Detroit, MI,, 9-12 May 1995, pp. 1984–1987.
[6]S. Mallat and Z. Zhang, “Matching pursuits with time frequency dictionaries,” IEEE Trans. on SP., vol. 41, pp. 3397ˆa3415, 1993.
[7]Y.C. Pati, Ramin Rezaiifar, and P.S. Krishnaprasad, “Orthogonal matching pursuit : Recursive function approximation with applications to wavelet decomposition,” in the 27 th Annual Asilomar, Pacific Grove, CA, USA, 1993.
[8]Soussen C., J. Idier, D. Brie, and J. Duan, “From bernoulligaussian deconvolution to sparse signal restoration,” IEEE Trans. on SP, vol. 59, no. 10, pp. 4572–4584, oct 2011.