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A highly adaptive directional time–frequency distribution

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Abstract

This paper presents an automatic method of computing a high-resolution adaptive time–frequency distribution. A recently developed locally adaptive directional time–frequency distribution (ADTFD) achieves high energy concentration and cross-term suppression, but it requires manual tuning of certain parameters. One set of parameters is not applicable to all types of signals. Moreover, the ADTFD fails to achieve optimum results when a given signal has both short-duration signal components and close components. This paper overcomes the limitation of the ADTFD by locally adapting the shape of the filter. Experimental results demonstrate the efficacy of the proposed approach for a large class of signals.

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References

  1. Bastiaans, M., Alieva, T., Stanković, L.: On rotated time–frequency kernels. Signal Process. Lett., IEEE 9(11), 378–381 (2002). doi:10.1109/LSP.2002.805118

    Article  Google Scholar 

  2. Boashash, B., Khan, N.A., Ben-Jabeur, T.: Time–frequency features for pattern recognition using high-resolution tfds. Digit. Signal Process. 40(C), 1–30 (2015)

    Article  MathSciNet  Google Scholar 

  3. Iatsenko, D., McClintock, P.V., Stefanovska, A.: Linear and synchrosqueezed time–frequency representations revisited: overview, standards of use, resolution, reconstruction, concentration, and algorithms. Digit. Signal Process. 42, 1–26 (2015). doi:10.1016/j.dsp.2015.03.004

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  5. Khan, N., Boashash, B.: Instantaneous frequency estimation of multicomponent nonstationary signals using multiview time–frequency distributions based on the adaptive fractional spectrogram. Signal Process. Lett., IEEE 20(2), 157–160 (2013)

    Article  Google Scholar 

  6. Khan, N.A., Boashash, B.: Multi-component instantaneous frequency estimation using locally adaptive directional time frequency distributions. Int. J. Adapt. Control Signal Process. (2015). doi:10.1002/acs.2583

    MathSciNet  Google Scholar 

  7. Malnar, D., Sucic, V., O’Toole, J.: Automatic quality assessment and optimisation of quadratic time–frequency representations. Electron. Lett. 51(13), 1029–1031 (2015)

    Article  Google Scholar 

  8. Pikula, S.: Verification of enhanced interference reduction in wvd on real non-stationary acoustic signal. In: Carpathian control conference (ICCC), 2015 16th international, pp 385–388 (2015). doi:10.1109/CarpathianCC.2015.7145109

  9. Stanković, L.: A measure of some time–frequency distributions concentration. Signal Process. 81(3), 621–631 (2001). Special section on Digital Signal Processing for Multimedia

    Article  MATH  Google Scholar 

  10. Stanković, L., Daković, M., Thayaparan, T.: Time–frequency signal analysis with applications. Artech House Radar. Artech House, Incorporated, Norwood (2013)

    Google Scholar 

  11. Zheng, L., Shi, D., Zhang, J.: Caf-frft: a center-affine-filter with fractional fourier transform to reduce the cross-terms of wigner distribution. Signal Process. 94, 330–338 (2014)

    Article  Google Scholar 

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Authors and Affiliations

  1. Laboratory of Advanced Industrial Signal Processing and AI, Shahrood University of Technology, Shahrood, Iran

    Mokhtar Mohammadi & Ali Akbar Pouyan

  2. Department of Electrical Engineering, Federal Urdu University, Islamabad, Pakistan

    Nabeel Ali Khan

Authors
  1. Mokhtar Mohammadi
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  2. Ali Akbar Pouyan
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  3. Nabeel Ali Khan
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Correspondence to Mokhtar Mohammadi.

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Mohammadi, M., Pouyan, A.A. & Khan, N.A. A highly adaptive directional time–frequency distribution. SIViP 10, 1369–1376 (2016). https://doi.org/10.1007/s11760-016-0901-x

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  • DOI: https://doi.org/10.1007/s11760-016-0901-x

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