Robust time-frequency analysis based on the L-estimation and compressive sensing

IEEE Signal Processing Letters

Volumn 20, Issue 5, 2013, Pages 499-502

  Stankovic, L ^a   Stankovic, S ^a,b   Orovic, I ^a   Amin, Moeness G ^c  

^a UNIVERSITY OF MONTENEGRO   (Montenegro)

^b VILLANOVA UNIVERSITY   (United States)

^c Villanova University   (United States)

Author keywords

compressive sensing; L estimation signal reconstruction; Robust time frequency distributions

Indexed keywords

COMPRESSIVE SENSING; L-STATISTICS; LOCAL AUTO-CORRELATION; NOISE FREE SIGNALS; SPECTRAL CHARACTERISTICS; TIME FREQUENCY ANALYSIS; TIME-FREQUENCY DISTRIBUTIONS; TIME-FREQUENCY REPRESENTATIONS;

ESTIMATION; IMPULSE NOISE; SIGNAL ANALYSIS;

SIGNAL RECONSTRUCTION;

EID: 84875704272     PISSN: 10709908     EISSN: None     Source Type: Journal    
DOI: 10.1109/LSP.2013.2252899     Document Type: Article

References (17)

1. V. Katkovnik, "RobustM-periodogram," IEEE Trans. Signal Process., vol. 46, no. 11, pp. 3104-3109, 1998.
2. I. Djurovi, L. Stankovi, and J. F. Bohme, "Robust L-estimation based forms of signal transforms and time-frequency representations," IEEE Trans. Signal Process., vol. 51, no. 7, pp. 1753-1761, 2003.
3. G. Viswanath and T. V. Sreenivas, "IF estimation using higher order TFRs," Signal Process., vol. 82, no. 2, pp. 127-132, 2002. (Pubitemid 34181569)
4. L. Stankovic, M. Dakovic, and T. Thayaparan, Time-Frequency Signal Analysis With Applications. Boston, MA, USA: Artech House, 2013.
5. B. Boashash and P. J. O'Shea, "PolynomialWigner-Ville distributions and their relationship to time-varying higher order spectra," IEEE Trans. Signal Process., vol. 42, no. 1, pp. 216-220, 1994.
6. J. Lerga and V. Sucic, "Nonlinear IF estimation based on the pseudo WVD adapted using the improved sliding pairwise ICI rule," IEEE Signal Process. Lett., vol. 16, no. 8, pp. 953-956, 2009.
7. D. Donoho, "Compressed sensing," IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, 2006.
8. R. Baraniuk, "Compressive sensing," IEEE Signal Process. Mag., vol. 24, no. 4, pp. 118-121, 2007.
9. X. Zeng and L. Yang, "Mixed impulse and Gaussian noise removal using detail-preserving regularization," Opt. Eng., vol. 49, no. 9, p. 097002, 2010.
10. R.H. Chan, X.W. Ho, and M. Nikolova, "Salt-and-paper noise removal by median-type noise detectors and detail-preserving regularization," IEEE Trans. Image Process., vol. 14, no. 10, pp. 1479-1485.
11. F. Ahmad and M. G. Amin, "Through-the-wall human motion indication using sparsity-driven change detection," IEEE Trans. Geosci. Remote Sensing, vol. 51, no. 2, pp. 881-890, 2013.
12. P. Flandrin and P. Borgnat, "Time-frequency energy distributions meet compressed sensing," IEEE Trans. Signal Process., vol. 58, no. 6, pp. 2974-2982.
13. M. Lustig, D. Donoho, and J. M. Pauly, "Sparse MRI: The application of compressed sensing for rapid MR imaging,"Magn. Res. Med., vol. 58, no. 6, pp. 1182-1195, 2007. (Pubitemid 350234205)
14. J. Meng, J. Ahmadi-Shokouh, H. Li, Z. Han, S. Noghanian, and E. Hossain, "Sampling rate reduction for 60 GHz UWB communication using compressive sensing," in Asilomar Conf. on Signals, Systems & Computers, Monterey, CA, USA, 2009.
15. R. E. Carrillo, K. E. Barner, and T. C. Aysal, "Robust sampling and reconstruction methods for sparse signals in the presence of impulsive noise," IEEE J. Sel. Topics Signal Process., vol. 4, no. 2, pp. 392-408, 2010.
16. J. L. Paredes and G. R. Arce, "Compressive sensing signal reconstruction by weighted median regression estimates," IEEE Trans. Signal Process., vol. 59, no. 6, pp. 2585-2260, 2011.
17. S. Stankovic, I. Orovic, and E. Sejdic, Multimedia Signals and Systems. Berlin, Germany: Springer, 2012.