Skip to main content
SpringerLink
Log in

Blind Source Separation Schemes for Mono-sensor and Multi-sensor Systems with Application to Signal Detection

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

In this paper, blind source separation (BSS) techniques based on time–frequency (t–f) distributions are proposed for multi-sensor and mono-sensor scenarios. The proposed schemes use t–f filtering and high-resolution t–f distributions to extract source signals that have very close components in the t–f domain. Through numerical simulations, the performance of the proposed schemes is compared with the existing algorithms. Results show that the proposed method outperforms the traditional BSS methods. In addition, the proposed BSS is applied to detect a presence of a non-stationary signal in a scenario, when noise power is unknown. Detection performance is compared with the existing detection methods through numerical simulations that show the proposed method performs better than the existing methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. A. Aissa-El-Bey, N. Linh-Trung, K. Abed-Meraim, A. Belouchrani, Y. Grenier, Underdetermined blind separation of nondisjoint sources in the time–frequency domain. IEEE Trans. Signal Process. 55(3), 897–907 (2007)

    Article  MathSciNet  Google Scholar 

  2. A. Aissa-El-Bey, N. Linh-Trung, K. Abed-Meraim, A. Belouchrani, Y. Grenier, Underdetermined blind separation of nondisjoint sources in the time–frequency domain. IEEE Trans. Signal Process. 55(3), 897–907 (2007)

    Article  MathSciNet  Google Scholar 

  3. S. Ali, D. Ramírez, M. Jansson, G. Seco-Granados, J.A. López-Salcedo, Multi-antenna spectrum sensing by exploiting spatio-temporal correlation. EURASIP J. Adv. Signal Process. 2014(1), 1–16 (2014)

    Article  Google Scholar 

  4. S. Ali, G. Seco-Granados, J.A. Lopez-Salcedo, Spectrum sensing with spatial signatures in the presence of noise uncertainty and shadowing. EURASIP J. Wirel. Commun. Netw. 2013(1), 1–16 (2013)

  5. M.G. Amin, Y. Zhang, Direction finding based on spatial time–frequency distribution matrices. Digit. Signal Process. 10(4), 325–339 (2000)

    Article  Google Scholar 

  6. A. Belouchrani, M.G. Amin, N. Thirion-Moreau, Y.D. Zhang, Source separation and localization using time–frequency distributions: an overview. IEEE Signal Process. Mag. 30(6), 97–107 (2013)

    Article  Google Scholar 

  7. B. Boashash, G. Azemi, N.A. Khan, Principles of time–frequency feature extraction for change detection in non-stationary signals: applications to newborn EEG abnormality detection. Pattern Recognit. 48(3), 616–627 (2015)

    Article  Google Scholar 

  8. B. Boashash, L. Boubchir, G. Azemi, A methodology for time–frequency image processing applied to the classification of non-stationary multichannel signals using instantaneous frequency descriptors with application to newborn EEG signals. EURASIP J. Adv. Signal Process. 2012(1), 1–16 (2012)

  9. B. Boashash, N. Ali Khan, T. Ben-Jabeur, Time–frequency features for pattern recognition using high-resolution TFDs: a tutorial review. Digit. Signal Process. 40, 1–30 (2015)

    Article  MathSciNet  Google Scholar 

  10. G. Chabriel, M. Kleinsteuber, E. Moreau, H. Shen, P. Tichavsky, A. Yeredor, Joint matrices decompositions and blind source separation: a survey of methods, identification, and applications. IEEE Signal Process. Mag. 31(3), 34–43 (2014)

    Article  Google Scholar 

  11. R. Chai, G. Naik, T.N. Nguyen, S. Ling, Y. Tran, A. Craig, H. Nguyen, Driver fatigue classification with independent component by entropy rate bound minimization analysis in an EEG-based system. IEEE J. Biomed. Health Inf. (2016). doi:10.1109/JBHI.2016.2532354

  12. Y. Chi, Guaranteed blind sparse spikes deconvolution via lifting and convex optimization. IEEE J. Sel. Top. Signal Process. 10(4), 782–794 (2016)

    Article  Google Scholar 

  13. P. Flandrin, Time–frequency filtering based on spectrogram zeros. IEEE Signal Process. Lett. 22(11), 2137–2141 (2015)

    Article  Google Scholar 

  14. K. Ghartey, A. Papandreou-Suppappola, D. Cochran, Time-varying techniques for multisensor signal detection. IEEE Trans. Signal Process. 54(9), 3353–3362 (2006)

    Article  Google Scholar 

  15. K. Ghribi, M. Djendi, D. Berkani, A wavelet-based forward bss algorithm for acoustic noise reduction and speech enhancement. Appl. Acoust. 105, 55–66 (2016)

    Article  Google Scholar 

  16. L. Guo, Y.D. Zhang, Q. Wu, M.G. Amin, Doa estimation of sparsely sampled nonstationary signals, in IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP) (July 2015), pp. 300–304

  17. Y. Guo, S. Huang, Y. Li, G.R. Naik, Edge effect elimination in single-mixture blind source separation. Circuits Syst. Signal Process. 32(5), 2317–2334 (2013)

    Article  MathSciNet  Google Scholar 

  18. Y. Guo, G.R. Naik, H. Nguyen, Single channel blind source separation based local mean decomposition for biomedical applications, in 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2013), pp. 6812–6815

  19. P. Heidenreich, L.A. Cirillo, A.M. Zoubir, Morphological image processing for FM source detection and localization. Signal Process. 89(6), 1070–1080 (2009)

    Article  MATH  Google Scholar 

  20. D.L. Jones, R.G. Baraniuk, An adaptive optimal-kernel time–frequency representation. IEEE Trans. Signal Process. 43(10), 2361–2371 (1995)

    Article  Google Scholar 

  21. N.A. Khan, B. Boashash, Multi-component instantaneous frequency estimation using locally adaptive directional time frequency distributions. Int. J. Adapt. Control Signal Process. 30, 429–442 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  22. N.A. Khan, I.A. Taj, M.N. Jaffri, S. Ijaz, Cross-term elimination in wigner distribution based on 2D signal processing techniques. Signal Process. 91(3), 590–599 (2011)

    Article  MATH  Google Scholar 

  23. N.A. Khan, S. Ali, M. Jansson, Direction of arrival estimation using adaptive directional time–frequency distributions. Multidimens. Syst. Signal Process. (2016). doi:10.1007/s11045-016-0435-y

  24. L. Le, S. Krishnan, Time–frequency signal synthesis and its application in multimedia watermark detection. EURASIP J. Adv. Signal Process. (1), 1–14 (2006)

  25. Z. Li, X. Yan, Z. Tian, C. Yuan, Z. Peng, L. Li, Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis. Measurement 46(1), 259–271 (2013)

    Article  Google Scholar 

  26. N. Linh-Trung, A. Belouchrani, K. Abed-Meraim, B. Boashash, Separating more sources than sensors using time–frequency distributions. EURASIP J. Appl. Signal Process. 2005, 2828–2847 (2005)

    Article  MATH  Google Scholar 

  27. S. Meignen, T. Oberlin, S. McLaughlin, Multicomponent signal denoising with synchrosqueezing, in 2012 IEEE Statistical Signal Processing Workshop (SSP) (2012), pp. 660–663

  28. M. Mohammadi, A. Akbar Pouyan, N. Ali Khan, A highly adaptive directional time–frequency distribution. Signal Image Video Process. 10(7), 1369–1376 (2016)

    Article  Google Scholar 

  29. M. Weifeng, M.G. Amin, Y. Zhang, Bilinear signal synthesis in array processing. IEEE Trans. Signal Process. 51(1), 90–100 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  30. G.R. Naik, Enhancement of the ill-conditioned original recordings using novel ICA technique. Int. J. Electr. 99(7), 899–906 (2012)

    Article  Google Scholar 

  31. G.R. Naik, K.G. Baker, H.T. Nguyen, Dependence independence measure for posterior and anterior EMG sensors used in simple and complex finger flexion movements: evaluation using SDICA. IEEE J. Biomed. Health Inf. 19(5), 1689–1696 (2015)

    Article  Google Scholar 

  32. G.R. Naik, D.K. Kumar, Estimation of independent and dependent components of non-invasive EMG using fast ICA: validation in recognising complex gestures. Comput. Methods Biomech. Biomed. Eng. 14(12), 1105–1111 (2011)

    Article  Google Scholar 

  33. G.R. Naik, S. Easter Selvan, H.T. Nguyen, Single-channel EMG classification with ensemble-empirical-mode-decomposition-based ICA for diagnosing neuromuscular disorders. IEEE Trans. Neural Syst. Rehabil. Eng. 24(7), 734–743 (2016)

    Article  Google Scholar 

  34. G. Pendharkar, G.R. Naik, H.T. Nguyen, Using blind source separation on accelerometry data to analyze and distinguish the toe walking gait from normal gait in ITW children. Biomed. Signal Process. Control 13, 41–49 (2014)

    Article  Google Scholar 

  35. L. Rankine, M. Mesbah, B. Boashash, IF estimation for multicomponent signals using image processing techniques in the time–frequency domain. Signal Process. 87(6), 1234–1250 (2007)

    Article  MATH  Google Scholar 

  36. G. Rilling, P. Flandrin, P. Goncalves, On empirical mode decomposition and its algorithms, in IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, vol. 3, (2003), pp. 8–11

  37. Z. Shan, J. Swary, S. Aviyente, Underdetermined source separation of EEG signals in the time–frequency domain, in IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008 (March 2008), pp. 3637–3640

  38. X. Sun, J. Wang, R. Chen, L. Kong, M.F.H. She, Directional Gaussian filter-based LBP descriptor for textural image classification. Procedia Eng. (CEIS) 15, 1771–1779 (2011)

    Article  Google Scholar 

  39. H.L. Van Trees, Optimum Array Processing (Detection, Estimation, and Modulation Theory, Part IV) (Wiley-Interscience, New York, 2002)

    Google Scholar 

  40. A.V.D. Veen, Blind separation of BPSK sources with residual carriers. Signal Process. 73, 67–79 (1999)

    Article  MATH  Google Scholar 

  41. A.V.D. Veen, S. Talwar, A. Paulraj, A subspace approach to blind space–time signal processing for wireless communication systems. IEEE Trans. Signal Process. 45, 173–190 (1997)

    Article  Google Scholar 

  42. Y. Yang, X. Dong, Z. Peng, W. Zhang, G. Meng, Component extraction for non-stationary multi-component signal using parameterized de-chirping and band-pass filter. IEEE Signal Process. Lett. 22(9), 1373–1377 (2015)

    Article  Google Scholar 

  43. Y.D. Zhang, M.G. Amin, B. Himed. Direction-of-arrival estimation of nonstationary signals exploiting signal characteristics, in 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) (2012), pp. 1223–1228

  44. Y.D. Zhang, L. Guo, Q. Wu, M.G. Amin, Multi-sensor kernel design for time–frequency analysis of sparsely sampled nonstationary signals, in IEEE Radar Conference (RadarCon) (May 2015), pp. 0896–0900

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Pakistan

    Sadiq Ali

  2. Electrical Engineering, Foundation University, Islamabad, Pakistan

    Nabeel Ali Khan & Muhammad Haneef

  3. SIST, ShanghaiTech University, Shanghai, China

    Xiliang Luo

Authors
  1. Sadiq Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nabeel Ali Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Haneef
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiliang Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sadiq Ali.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ali, S., Khan, N.A., Haneef, M. et al. Blind Source Separation Schemes for Mono-sensor and Multi-sensor Systems with Application to Signal Detection. Circuits Syst Signal Process 36, 4615–4636 (2017). https://doi.org/10.1007/s00034-017-0533-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-017-0533-6

Keywords

Search

Navigation