Extension of FBSE-EWT for Complex Signal Analysis

In this letter, a new framework based on Fourier- Bessel series expansion (FBSE)-empirical wavelet transform (EWT) is proposed with the aim of analyzing complex signals. The critical part of this framework is to separate the real- time equivalent positive and negative frequency components of complex signals using suitable filters and then decompose both the real-time signals into their corresponding intrinsic mode functions (IMFs) using the FBSE-EWT technique. After that, the obtained IMFs are represented in a set of complex IMFs (CIMFs) using the Hilbert transform. In the end, joint time-frequency distribution (TFD) is obtained using Hilbert spectral analysis of CIMFs. Two synthetic multicomponent complex signals are considered for simulation purposes, along with a real-world wind signal. The results of proposed framework compared with complex empirical mode decomposition (CEMD) and complex iterative eigenvalue decomposition of Hankel matrix (CIEVDHM) and found to be providing a better result.