Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

A Modified Viterbi Algorithm-Based IF Estimation Algorithm for Adaptive Directional Time–Frequency Distributions

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

Abstract

Time–frequency (TF)-based instantaneous frequency estimation algorithms fail to achieve the desired performance when the underlying TF distribution suffers from low resolution of the signal components or signal components intersect each other in the TF domain. This paper addresses above-mentioned problems by (a) employing adaptive directional time–frequency distributions for resolving close components and (b) developing a variant of the Viterbi algorithm that employs both the direction and amplitude of the signal components for IF estimation of crossing components at low signal-to-noise ratio. Experimental results indicate that the proposed method outperforms state-of-the-art methods such as original Viterbi-based IF estimation algorithm and ridge path regrouping methods.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

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

Similar content being viewed by others

References

  1. S. Ali, N. Khan, M. Haneef, X. Luo, Blind source separation schemes for mono-sensor and multi-sensor systems with application to signal detection. Circuits Syst. Signal Process. 36(11), 4615–4636 (2017)

    Article  MathSciNet  Google Scholar 

  2. M.G. Amin, D. Borio, Y. Zhang, L. Galleani, Time–frequency analysis for GNSSs: from interference mitigation to system monitoring. IEEE Signal Process. Mag. 34(5), 85–95 (2017)

    Article  Google Scholar 

  3. F. Auger, P. Flandrin, Y.-T. Lin, S. McLaughlin, S. Meignen, T. Oberlin, H.-T. Wu, Time–frequency reassignment and synchrosqueezing: an overview. IEEE Signal Process. Mag. 30(6), 32–41 (2013)

    Article  Google Scholar 

  4. B. Barkat, K. Abed-Meraim, Algorithms for blind components separation and extraction from the time–frequency distribution of their mixture. EURASIP J. Adv. Signal Process. 2004, 978487 (2004)

    Article  Google Scholar 

  5. B. Boashash, N.A. 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 

  6. S. Chen, X. Dong, G. Xing, Z. Peng, W. Zhang, G. Meng, Separation of overlapped non-stationary signals by ridge path regrouping and intrinsic chirp component decomposition. IEEE Sens. J. 17(18), 5994–6005 (2017)

    Article  Google Scholar 

  7. C. Conru, I. Djurović, C. Ioana, L. Stanković, Time–frequency detection using Gabor filter bank and Viterbi based grouping algorithm, in IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) (2005)

  8. K. Czarnecki, The instantaneous frequency rate spectrogram. Mech. Syst. Signal Process. 66, 361–373 (2016)

    Article  Google Scholar 

  9. K. Czarnecki, D. Fourer, F. Auger, M. Rojewski, A fast time–frequency multi-window analysis using a tuning directional kernel. Signal Process. 147, 110–119 (2018)

    Article  Google Scholar 

  10. I. Djurović, QML-RANSAC instantaneous frequency estimator for overlapping multicomponent signals in the time–frequency plane. IEEE Signal Process. Lett. 25(3), 447–451 (2018)

    Article  Google Scholar 

  11. I. Djurović, L. Stanković, An algorithm for the Wigner distribution based instantaneous frequency estimation in a high noise environment. Signal Process. 84(3), 631–643 (2004)

    Article  MATH  Google Scholar 

  12. I. Djurović, L. Stanković, Modification of the ICI rule-based IF estimator for high noise environments. IEEE Trans. Signal Process. 52(9), 2655–2661 (2004)

    Article  Google Scholar 

  13. X. Dong, S. Chen, G. Xing, Z. Peng, W. Zhang, G. Meng, Doppler frequency estimation by parameterized time–frequency transform and phase compensation technique. IEEE Sens. J. 18(9), 3734–3744 (2018)

    Article  Google Scholar 

  14. M.K. Emresoy, A. El-Jaroudi, Iterative instantaneous frequency estimation and adaptive matched spectrogram. Signal Process. 64(2), 157–165 (1998)

    Article  MATH  Google Scholar 

  15. F. Hlawatsch, F. Boudreaux-Bartels, Linear and quadratic time–frequency signal representations. IEEE Signal Process. Mag. 9(2), 21–67 (1992)

    Article  Google Scholar 

  16. N.E. Huang, Z. Shen, S.R. Long, M.C. Wu, H.H. Shih, Q. Zheng, N.-C. Yen, C.C. Tung, H.H. Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, in Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol 454 (The Royal Society, 1998), p. 903–995

  17. V. Katkovnik, L. Stanković, Instantaneous frequency estimation using the Wigner distribution with varying and data-driven window length. IEEE Trans. Signal Process. 46(9), 2315–2325 (1998)

    Article  Google Scholar 

  18. N.A. Khan, S. Ali, Sparsity-aware adaptive directional time–frequency distribution for source localization. Circuits Syst. Signal Process. 37(3), 1223–1242 (2018)

    Article  MathSciNet  Google Scholar 

  19. N. Khan, B. Boashash, Multi-component instantaneous frequency estimation using locally adaptive directional time frequency distributions. Int. J. Adapt. Control Signal Process. 30(3), 429–442 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  20. N. Khan, P. Jonsson, M. Sandsten, Performance comparison of time–frequency distributions for estimation of instantaneous frequency of heart rate variability signals. Appl. Sci. 7(3), 1–16 (2017)

    Google Scholar 

  21. N.A. Khan, S. Ali, A new feature for the classification of non-stationary signals based on the direction of signal energy in the time–frequency domain. Comput. Biol. Med. 100, 10–16 (2018)

    Article  Google Scholar 

  22. P. Li, Q.-H. Zhang, An improved Viterbi algorithm for IF extraction of multicomponent signals. Signal Image Video Process. 12(1), 171–179 (2017)

    Article  Google Scholar 

  23. F. Lurz, S. Lindner, S. Linz, S. Mann, R. Weigel, A. Koelpin, High-speed resonant surface acoustic wave instrumentation based on instantaneous frequency measurement. IEEE Trans. Instrum. Meas. 66(5), 974–984 (2017)

    Article  Google Scholar 

  24. D. Mikluc, D. Bujaković, M. Andrić, S. Simić, Estimation and extraction of radar signal features using modified B distribution and particle filters. J. RF Eng. Telecommun. 70(9–10), 417–427 (2016)

    Google Scholar 

  25. M. Mohammadi, N. Khan, A.A. Pouyan, Automatic seizure detection using a highly adaptive directional time-frequency distribution. Multidimens. Syst. Signal Process. 29(4), 1661–1678 (2018)

    Article  Google Scholar 

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

    Article  Google Scholar 

  27. M. Mohammadi, A.A. Pouyan, N. Khan, V. Abolghasemi, Locally optimized adaptive directional time-frequency distributions. Circuits Syst. Signal Process. 37(8), 3154–3174 (2018)

    Article  MathSciNet  Google Scholar 

  28. T.B. Patel, H.A. Patil, Cochlear filter and instantaneous frequency based features for spoofed speech detection. IEEE J. Sel. Top. Signal Process. 11(4), 618–631 (2017)

    Article  Google Scholar 

  29. 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 

  30. S. Sandoval, P.L. De Leon, Advances in empirical mode decomposition for computing instantaneous amplitudes and instantaneous frequencies, in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2017), p. 4311–4315

  31. L. Stanković, M. Daković, T. Thayaparan, Time–Frequency Signal Analysis with Applications (Artech House, Boston, 2013)

    MATH  Google Scholar 

  32. L. Stanković, I. Djurović, S. Stanković, M. Simeunović, S. Djukanović, M. Daković, Instantaneous frequency in time–frequency analysis: enhanced concepts and performance of estimation algorithms. Digit. Signal Process. 2, 1–13 (2014)

    Article  Google Scholar 

  33. C. Wang, F. Kong, Q. He, F. Hu, F. Liu, Doppler effect removal based on instantaneous frequency estimation and time domain re-sampling for wayside acoustic defective bearing detector system. Measurement 50, 346–355 (2014)

    Article  Google Scholar 

  34. 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 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nabeel Ali Khan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khan, N.A., Mohammadi, M. & Djurović, I. A Modified Viterbi Algorithm-Based IF Estimation Algorithm for Adaptive Directional Time–Frequency Distributions. Circuits Syst Signal Process 38, 2227–2244 (2019). https://doi.org/10.1007/s00034-018-0960-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-018-0960-z

Keywords

Navigation