Skip to main content
Log in

Adaptive Time Delay Estimation Based on Signal Preprocessing and Fourth-Order Cumulant

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

Abstract

To address the problem that the traditional generalized cross-correlation (GCC) method has poor delay estimation accuracy in the low signal-to-noise ratio (SNR) environment or complex noise background, an adaptive time delay estimation algorithm based on signal preprocessing and fourth-order cumulant is proposed. We first preprocess the noisy signal using singular value decomposition and wavelet denoising. Next, we use an improved variable step-size least mean square algorithm based on multi-scale wavelet transform to iteratively operate on the one-dimensional slice of fourth-order cumulant. Finally, we derive the time delay from the peak offset of the filter weight coefficient. The simulation results show that the proposed method outperforms the GCC method and the fourth-order cumulant GCC method in Gaussian non-correlated noise, Gaussian correlated noise, and non-Gaussian noise backgrounds, achieving relatively accurate estimation results even in a low SNR environment. This technique could offer a new approach for detecting weak signals and passively locating small targets in ocean exploration.

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

Access this article

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
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. M. Azaria, D. Hertz, Time delay estimation by generalized cross correlation methods. IEEE Trans. Acoust. Speech Signal Process. 32(2), 280–285 (1984). https://doi.org/10.1109/TASSP.1984.1164314

    Article  Google Scholar 

  2. T.P. Bhardwaj, R. Nath, Maximum likelihood estimation of time delays in multipath acoustic channel. Signal Process. 90(5), 1750–1754 (2010). https://doi.org/10.1016/j.sigpro.2009.11.023

    Article  MATH  Google Scholar 

  3. V. Bruni, D. Vitulano, Wavelet-based signal de-noising via simple singularities approximation. Signal Process. 86(4), 859–876 (2006). https://doi.org/10.1016/j.sigpro.2005.06.017

    Article  MATH  Google Scholar 

  4. M.V. Dokic, P.M. Clarkson, Real-time adaptive filters for time-delay estimation. Mech. Syst. Signal Process. 6(5), 403–418 (1992). https://doi.org/10.1016/0888-3270(92)90065-Q

    Article  Google Scholar 

  5. D.L. Donoho, J.M. Johnstone, Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3), 425–455 (1994). https://doi.org/10.1093/biomet/81.3.425

    Article  MathSciNet  MATH  Google Scholar 

  6. R. Henry, J. Hofrichter, Singular value decomposition: application to analysis of experimental data. Methods Enzymol 210(2), 129–192 (1992). https://doi.org/10.1016/0076-6879(92)10010-B

    Article  Google Scholar 

  7. K. Ho-Wuk, P. Hong-Sug, L. Sang-Kwon, Modified-filtered-u LMS algorithm for active noise control and its application to a short acoustic duct. Mech. Syst. Signal Process. 25(1), 475–784 (2011). https://doi.org/10.1016/j.ymssp.2010.09.001

    Article  Google Scholar 

  8. C. Knapp, G. Carter, The generalized correlation method for estimation of time delay. IEEE Trans. Acoust. Speech Signal Process. 24(4), 320–327 (1976). https://doi.org/10.1109/TASSP.1976.1162830

    Article  Google Scholar 

  9. Z.X. Li, W.X. Dai, Local mean decomposition combined with SVD and application in telemetry vibration signal processing. Appl. Mech. Mater. 347(2), 854–858 (2013). https://doi.org/10.4028/www.scientific.net/AMM.347-350.854

    Article  Google Scholar 

  10. F. Liu, Y. Zhang, X. Yang, Parametric multipath time delay estimation based on fourth-order cumulants and singular value decomposition. Electron. Inform. Warfare Technol. 24(5), 16–19 (2009)

    Google Scholar 

  11. S. Mallat, W.L. Hwang, Singularity detection and processing with wavelets. IEEE Trans. Inform. Theory 38(2), 617–643 (1992). https://doi.org/10.1109/18.119727

    Article  MathSciNet  MATH  Google Scholar 

  12. J.M. Mendel, Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications. Proc. IEEE 79(3), 278–305 (1991). https://doi.org/10.1109/5.75086

    Article  Google Scholar 

  13. M. Nasri, H. Nezamabadi, Image denoising in the wavelet domain using a new adaptive thresholding function. Neurocomputing 72(4), 1012–1025 (2009). https://doi.org/10.1016/j.neucom.2008.04.016

    Article  Google Scholar 

  14. C.L. Nikias, J.M. Mendel, Signal processing with higher-order spectra. IEEE Signal Process. Mag. 10(3), 10–37 (1993). https://doi.org/10.1109/79.221324

    Article  Google Scholar 

  15. C.L. Nikias, R. Pan, Time delay estimation in unknown Gaussian spatially correlated noise. IEEE Trans. Acoust. Speech Signal Process. 36(11), 1706–1714 (1988). https://doi.org/10.1109/29.9008

    Article  MATH  Google Scholar 

  16. Z.W. Qian, L. Cheng, Y.H. Li, Noise reduction method based on singular value decomposition. J. Vib. Meas. Diagn. 31(4), 459–463 (2011). https://doi.org/10.16450/j.cnki.issn.1004-6801.2011.04.010

    Article  Google Scholar 

  17. A. Rajwade, A. Rangarajan, A. Banerjee, Image denoising using the higher order singular value decomposition. IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 849–862 (2013). https://doi.org/10.1109/TPAMI.2012.140

    Article  Google Scholar 

  18. F. Reed, P. Feintuch, N. Bershad, Time delay estimation using the LMS adaptive filter–static behaviour. IEEE Trans. Acoust. Speech Signal Process. 29(3), 561–571 (1981). https://doi.org/10.1109/TASSP.1981.1163614

    Article  Google Scholar 

  19. S. Sardy, P. Tseng, A. Bruce, Robust wavelet denoising. IEEE Trans. Signal Process. 49(6), 1146–1152 (2001). https://doi.org/10.1109/78.923297

    Article  Google Scholar 

  20. C. Sun, L. Li, W. Chen, Quadratic correlation time delay estimation algorithm based on Kaiser window and Hilbert transform. in Proceedings of the IEEE International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), Harbin, China, (2016), pp. 927–931. https://doi.org/10.1109/IMCCC.2016.149

  21. R. Tao, X.M. Li, Y.L. Li et al., Time-delay estimation of chirp signals in the fractional Fourier domain. IEEE Trans. Signal Process. 57(7), 2852–2855 (2009). https://doi.org/10.1109/TSP.2009.2020028

    Article  MathSciNet  MATH  Google Scholar 

  22. F.Q. Tian, R. Luo, A novel variable step size LMS algorithm based on modified hyperbolic tangent and its simulation. Adv. Mater. Res. 490(6), 1426–1430 (2012). https://doi.org/10.4028/www.scientific.net/AMR.490-495.1426

    Article  Google Scholar 

  23. J.K. Tugnait, Frequency domain adaptive filters using higher-order statistics with application to adaptive time delay estimation. Int. J. Adapt. Control Signal Process. 10(2), 137–157 (1996). https://doi.org/10.1002/(SICI)1099-1115(199603)10:2/3

    Article  MATH  Google Scholar 

  24. Y. Wu, A. R. Leyman, Time delay estimation using higher-order statistics: a set of new results. in Proceedings of the IEEE International Conference on Information, Communications and Signal Processing (ICICS), Singapore, vol. 3 (1997), pp. 1397–1400. https://doi.org/10.1109/ICICS.1997.652220.

  25. H.J. Wu, Y.M. Wen, P. Li, Dynamic discrimination of convergence of the LMS time delay estimation in complicated noisy environments. Appl. Acoust. 68(6), 628–641 (2007). https://doi.org/10.1016/j.apacoust.2006.03.011

    Article  Google Scholar 

  26. Q. Zhang, L. Zhang, An improved delay algorithm based on generalized cross correlation. in Proceeding of the IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, (2017), pp. 395–399. https://doi.org/10.1109/ITOEC.2017.8122323.

  27. X.Z. Zhao, B.Y. Ye, T.J. Chen, Principle of singular signal detection based on SVD and its application. J. Vib. Shock 21(6), 11–14 (2008). https://doi.org/10.13465/j.carolcarrollnkiJVS.2008.06.040

    Article  Google Scholar 

Download references

Funding

This research was supported by funds from the National Natural Science Foundation of China under Grant Numbers 41906005, 41705081, National Key Research and Development Project of China under Grant Numbers 2017YFB0202701, and National Basic Research Program of China under Grant Number 2019-JCJQ-ZD-149-00.

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization, BL and XZ; methodology, BL; software, BL; validation, SJ; formal analysis, SZ; data curation, DT; writing—original draft preparation, BL; writing—review and editing, BL; all authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Baoheng Liu.

Ethics declarations

Conflicts of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, B., Zhang, X., Jia, S. et al. Adaptive Time Delay Estimation Based on Signal Preprocessing and Fourth-Order Cumulant. Circuits Syst Signal Process 42, 6160–6181 (2023). https://doi.org/10.1007/s00034-023-02390-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-023-02390-7

Navigation