Multicomponent AM-FM signal separation and demodulation with null space pursuit

Signal, Image and Video Processing

Volumn 7, Issue 6, 2013, Pages 1093-1102

  Hu, Xiyuan ^a   Peng, Silong ^a   Hwang, Wen Liang ^b,c  

^a INSTITUTE OF AUTOMATION   (China)

^b INSTITUTE OF INFORMATION SCIENCE   (Taiwan)

^c KAINAN UNIVERSITY   (Taiwan)

Author keywords

Adaptive signal separation; Multicomponent AM FM signals; Null space pursuit (NSP); Signal demodulation

Indexed keywords

ADAPTIVE OPERATORS; ADAPTIVE SIGNALS; DIFFERENTIAL OPERATORS; INSTANTANEOUS FREQUENCY; MULTICOMPONENT AM-FM SIGNALS; NULL SPACE PURSUITS; OPTIMIZATION PROBLEMS; SIGNAL DEMODULATION;

AMPLITUDE MODULATION; MATHEMATICAL OPERATORS; OPTICAL VARIABLES MEASUREMENT; SEPARATION;

DEMODULATION;

EID: 84885924220     PISSN: 18631703     EISSN: 18631711     Source Type: Journal    
DOI: 10.1007/s11760-012-0354-9     Document Type: Article

References (33)

1. Airline passenger data (02/09/2011). http://www. stat. unc. edu/faculty/hurd/stat185Data/airpassdoc. html.
2. Auger F., Flandrin P.: Improving the readability of time-frequency and time-scale representations by the reassignment method. In: IEEE Trans. Signal Process. 43(5), 1068-1089 (1995).
3. Bovik A., Maragos P., Quatieri T. F.: Am-fm energy detection and separation in noise using multiband energy operators. In: IEEE Trans. Signal Process. 41(12), 3245-3265 (1993).
4. Carmona R., Hwang W. L., Torresani B.: Practical Time-Frequency Analysis. Academic Press, London (1998).
5. Carmona R., Hwang W. L., Torresani B.: Multiridge detection and time-frequency reconstruction. In: IEEE Trans. Signal Process. 47(2), 480-492 (1999).
6. Cohen, L.: What is a multicomponent signal? In: Proceedings of IEEE ICASSP '92, pp. 113-116 (1992).
7. Delprat N., Escudie B., Guilemain P., Kronland-Martinet R., Tchamitchian P., Torresani B.: Asymptotic wavelet and gabor analysis: extraction of instantaneous frequency. In: IEEE Trans. Inf. Theory 38(2), 644-664 (1992).
8. Diamantaras K. I.: A clustering approach for the blind separation of multiple finite alphabet sequences from a single linear mixture. Signal Process. 86(4), 877-891 (2006).
9. Diamantaras, K. I.: Blind separation of two multi-level sources from a single linear mixture. In: 18th IEEE Workshop on Machine Learning for Signal Processing (MLSP-2008), pp. 67-72. Cancun, Mexico (2008).
10. Diop E., Boudraa A., Salzenstein F.: A joint 2d AM-FM estimation based on higher order teager-kaiser energy operators. Signal Image Video Process. 5, 61-68 (2011).
11. Ensemble emd toolbox (02/09/2011) http://rcada. ncu. edu. tw/research1_clip_program. htm.
12. Gianfelici F., Biagetti G., Crippa P., Turchetti C.: Multicomponent am-fm representations: an asymptotically exact approach. In: IEEE Trans. Audio Speech Lang. Process. 15(3), 823-837 (2007).
13. Gupta, R., Kumar, A., Bahl, R.: Estimation of instantaneous frequencies using iterative empirical mode decomposition. Signal Image Video Process. (2012). doi: 10. 1007/s11760-012-0305-5.
14. Hanson H., Maragos P., Potamianos A.: A system for finding speech formants and modulations via energy separation. In: IEEE Trans. Speech Audio Process. 2(3), 436-443 (1994).
15. Hasan M., Apu M., Molla M.: A robust method for parameter estimation of ar systems using empirical mode decomposition. Signal Image Video Process. 4, 451-461 (2010).
16. Hu X., Peng S., Hwang W. L.: Estimation of instantaneous frequency parameters of the operator-based signal separation method. Adv. Adapt. Data Anal. 1(4), 573-586 (2009).
17. Hu X., Peng S., Hwang W. L.: Emd revisited: a new understanding of the envelope and resolving the mode-mixing problems in am-fm signals. In: IEEE Trans. Signal Process. 60(3), 1075-1086 (2012).
18. Huang N., Wu Z., Long S., Arnold K., Chen X., Blank K.: On instantaneous frequency. Adv. Adapt. Data Anal. 1(2), 177-229 (2009).
19. Huang N. E., Shen Z., Long S. R., Wu M. L., Shih H. H., Zheng Q., Yen N. C., Tung C. C., Liu H. H., Zhou X., Wong S. T. C.: The empirical mode decomposition and hilbert spectrum for nonlinear and nonstationary time series analysis. Proc. R Soc. Lond. A 454, 903-995 (1998).
20. Jabloun M., Leonard F., Vieira M., Martin N.: A new flexible approach to estimate the ia and if of nonstationary signals of long-time duration. In: IEEE Trans. Signal Process. 55(7), 3633-3644 (2007).
21. Li, P., Wang, D. C., Wang, L.: Separation of micro-doppler signals based on time frequency filter and viterbi algorithm. Signal Image Video Process. 1-13 (2011).
22. Maragos P., Kaiser J. F., Quatieri T. F.: On amplitude and frequency demodulation using energy operators. In: IEEE Trans. Signal Process. 41(4), 1532-1551 (1993).
23. Nashif A., Jones D.: Vibration Damping. Wiley-Interscience, New York (1985).
24. Peng S., Hwang W. L.: Adaptive signal decomposition based on local narrow band signals. In: IEEE Trans. Signal Process. 56(7), 2669-2676 (2008).
25. Peng S., Hwang W. L.: Null space pursuit: an operator-based approach to adaptive signal separation. In: IEEE Trans. Signal Process. 58(5), 2475-2483 (2010).
26. Pham D., Zoubir Z.: Analysis of multicomponent polynomial phase signals. In: IEEE Trans. Signal Process. 55(1), 56-65 (2007).
27. Restrepo A., Quiroga J.: Root and pre-constant signals of the 1d teager-kaiser operator. Signal Image Video Process. 5, 363-378 (2011).
28. Rostami, M., Babaie-Zadeh, M., Samadi, S., Jutten, C.: Blind source separation of discrete finite alphabet sources using a single mixture. In: Proceedings of IEEE Statistical Signal Processing Workshop (SSP2011), pp. 709-712. Nice, France (2011).
29. Santhanam B., Maragos P.: Multicomponent am-fm decomposition via periodicity-based algebraic separation and energy-based demodulation. In: IEEE Trans. Commun. 48(3), 473-490 (2000).
30. Søndergaard, P., Torrésani, B., Balazs, P.: The linear time-frequency analysis toolbox. Int. J. Wavelets Multiresolut. Inf. Process. (2012). doi: 10. 1142/S0219691312500324.
31. Virtanen T.: Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria. In: IEEE Trans. Audio Speech Lang. Process. 15(3), 1066-1074 (2007).
32. Wu Z., Huang N.: Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1(1), 1-41 (2009).
33. Zhang T., Fan Q.: Wavelet characterization of dyadic bmo norm and its application in image decomposition for distinguishing between texture and noise. Int. J. Wavelets Multiresolut. Inf. Proces. 9(3), 445-457 (2011).