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IF Estimation of Overlapped Multicomponent Signals Based on Viterbi Algorithm

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Abstract

Viterbi algorithm (VA) on time frequency (TF) distribution is a highly performed instantaneous frequency (IF) estimator. However, inaccurate IFs may be tracked due to switch problem in VA when signal components are overlapped on the TF plane. In order to address the problem, this paper first assumes the IF linearity in the overlapped TF regions should not change much, then, a new penalty function describing the variation of IF linearity based on the linear least square fitting technique is developed, and finally, a novel algorithm composed of two IF estimates is introduced. In the first coarse IF estimation, original VA is applied to determine the TF overlapped regions. In the second fine IF estimation, a modified VA employing the new penalty function is applied in the overlapped regions, while the original VA still functions in the non-overlapped regions. Simulations indicate the proposed algorithm can effectively suppress the switch problem and thus can achieve accuracy improvement especially for non-monotonous IF curves compared to other VA-based estimators.

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References

  1. X. Bai, M. Xing, F. Zhou et al., Imaging of micromotion targets with rotating parts based on empirical-mode decomposition. IEEE Trans. Geosci. Remote Sens. 46(11), 3514–3523 (2008)

    Article  Google Scholar 

  2. B. Boashash, Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals. Proc. IEEE 80(4), 520–538 (1992)

    Article  Google Scholar 

  3. S. Chen, X. Dong, G. Xing et al., 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 

  4. S. Chen, Y. Yang, K. Wei et al., Time-varying frequency-modulated component extraction based on parameterized demodulation and singular value decomposition. IEEE Trans. Instrum. Meas. 65(2), 276–285 (2016)

    Article  Google Scholar 

  5. V.C. Chen, F. Li, S.S. Ho et al., Micro-Doppler effect in radar: phenomenon, model, and simulation study. IEEE Trans. Aerosp. Electron. Syst. 42(1), 2–21 (2006)

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. I. Djurović, Estimation of sinusoidal frequency-modulated signal parameters in high-noise environment. SIViP 11(8), 1537–1541 (2017)

    Article  Google Scholar 

  8. 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 

  9. N.A. Khan, M. Mohammadi, I. Djurović, A modified Viterbi algorithm-based IF estimation algorithm for adaptive directional time–frequency distributions. Circuits Syst. Signal Process. 38(5), 2227–2244 (2019)

    Article  Google Scholar 

  10. P. Li, D.C. Wang, J.L. Chen, Parameter estimation for micro-Doppler signals based on cubic phase function. SIViP 7(6), 1239–1249 (2013)

    Article  Google Scholar 

  11. P. Li, D.C. Wang, L. Wang, Separation of micro-Doppler signals based on time frequency filter and Viterbi algorithm. SIViP 7(3), 593–605 (2013)

    Article  MathSciNet  Google Scholar 

  12. P. Li, Q.H. Zhang, An improved Viterbi algorithm for IF extraction of multicomponent signals. SIViP 12(1), 171–179 (2018)

    Article  Google Scholar 

  13. M. Mohammadi, A.A. Pouyan, N.A. Khan, A highly adaptive directional time–frequency distribution. SIViP 10(7), 1369–1376 (2016)

    Article  Google Scholar 

  14. H.J. Motulsky, L.A. Ransnas, Fitting curves to data using nonlinear regression: a practical and nonmathematical review. FASEB J. 1(5), 365–374 (1987)

    Article  Google Scholar 

  15. S.T.N. Nguyen, S. Kodituwakku, R. Melino et al., Wavelet-based sparse representation for helicopter main rotor blade radar backscatter signal separation. IEEE Trans. Aerosp. Electron. Syst. 53(6), 2936–2949 (2017)

    Article  Google Scholar 

  16. E. Sejdić, I. Orović, S. Stanković, Compressive sensing meets time–frequency: an overview of recent advances in time–frequency processing of sparse signals. Digit. Signal Proc. 77, 22–35 (2018)

    Article  MathSciNet  Google Scholar 

  17. P. Suresh, T. Thayaparan, T. Obulesu et al., Extracting micro-Doppler radar signatures from rotating targets using Fourier–Bessel transform and time–frequency analysis. IEEE Trans. Geosci. Remote Sens. 52(6), 3204–3210 (2014)

    Article  Google Scholar 

  18. Y. Yang, X. Dong, Z. Peng et al., 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 

  19. Y. Yang, Z.K. Peng, G. Meng et al., Characterize highly oscillating frequency modulation using generalized Warblet transform. Mech. Syst. Signal Process. 26, 128–140 (2016)

    Article  Google Scholar 

  20. H. Zhang, G. Bi, W. Yang et al., IF estimation of FM signals based on time–frequency image. IEEE Trans. Aerosp. Electron. Syst. 51(1), 326–343 (2015)

    Article  Google Scholar 

  21. Q. Zhang, T.S. Yeo, H.S. Tan et al., Imaging of a moving target with rotating parts based on the Hough transform. IEEE Trans. Geosci. Remote Sens. 46(1), 291–299 (2008)

    Article  Google Scholar 

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Acknowledgements

This work is supported by The Natural Science Foundation of the Jiangsu Higher Education Institutions of China (17KJB510027).

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Correspondence to Po Li.

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Li, P., Zhang, QH. IF Estimation of Overlapped Multicomponent Signals Based on Viterbi Algorithm. Circuits Syst Signal Process 39, 3105–3124 (2020). https://doi.org/10.1007/s00034-019-01314-8

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