I. Introduction
Awavelet transform is the popular time–frequency analysis technique that is being used in various applications, such as power electronics [1], power system engineering [2], biomedical signal processing [3], seismic data analysis [4], [5], [6], radar application [7], [8], communication engineering [9], and vibration signals modal analysis [10]. However, the wavelet transform decomposes the signal as the summation of the weights of the different scales and translation of the mother wavelet producing the spectrum of amplitude versus time scale, which is called scalogram. The complex harmonic wavelet (CHW) in [11] is such a wavelet transform that decomposes the signal in direct amplitude versus time–frequency called spectrogram, and its coefficients are computed using discrete Fourier transform (DFT) and inverse DFT (IDFT). However, in the case of DFT coefficients, the energy of each spectral component is spread over the whole frequency axis leading to a severe leakage from one band to another band [12], [13]. Therefore, the processing of coefficients of individual subbands in the case of CHW is ineffective, as the energy of one band has leaked to another [14]. Therefore, the application of CHW transform is limited due to its spectral leakage between its subbands.