Removing interference components in time–frequency representations using morphological operators

Time–frequency representations have been of great interest in the analysis and classification of non-stationary signals. The use of highly selective transformation techniques is a valuable tool for obtaining accurate information for studies of this type. The Wigner-Ville distribution has high time and frequency selectivity in addition to meeting some interesting mathematical properties. However, due to the bi-linearity of the transform, interference terms emerge when the transform is applied over multi-component signals. In this paper, we propose a technique to remove cross-components from the Wigner-Ville transform using image processing algorithms. The proposed method exploits the advantages of non-linear morphological filters, using a spectrogram to obtain an adequate marker for the morphological processing of the Wigner-Ville transform. Unlike traditional smoothing techniques, this algorithm provides cross-term attenuations while preserving time–frequency resolutions. Moreover, it could also be applied to distributions with different interference geometries. The method has been applied to a set of different time–frequency transforms, with promising results.

► Time–frequency representations are essential when working with non-stationary signals. ► We propose a technique to remove cross-components using image processing algorithms. ► The method provides cross-term attenuations while preserving time–frequency resolution. ► The proposed method works even when signal and interference terms overlap.