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An empirical wavelet transform-based approach for cross-terms-free Wigner–Ville distribution

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This paper presents an efficient methodology based on empirical wavelet transform (EWT) to remove cross-terms from the Wigner–Ville distribution (WVD). An EWT-based filter bank method is suggested to remove the cross-terms that occur due to nonlinearity in modulation. The mean-square error-based filter bank bandwidth selection is done which has been applied for the boundaries selection in EWT. In this way, a signal-dependent adaptive boundary selection is performed. Thereafter, energy-based segmentation is applied in time domain to eliminate inter-cross-terms generated between components. Moreover, the WVD of all the components is added together to produce a complete cross-terms-free time–frequency distribution. The proposed method is compared with other existing methods, and normalized Rényi entropy measure is also computed for validating the performance.

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Correspondence to Rishi Raj Sharma.

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Sharma, R.R., Kalyani, A. & Pachori, R.B. An empirical wavelet transform-based approach for cross-terms-free Wigner–Ville distribution. SIViP (2019). https://doi.org/10.1007/s11760-019-01549-7

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  • Time–frequency representation
  • Wigner–Ville distribution
  • Empirical wavelet transform
  • Cross-terms
  • Non-stationary signals