Augmented EMD for complex-valued univariate signals

• Main Authors: Oh, Beom-Seok, Zhuang, Huiping, Toh, Kar-Ann, Lin, Zhiping
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2020
• Subjects: Engineering::Electrical and electronic engineering; Doppler Radar; Feature Extraction
• Online Access: https://hdl.handle.net/10356/144639
• Institution: Nanyang Technological University

In this study, the authors propose an efficient extension of the standard empirical mode decomposition (EMD) for complex-valued univariate signal decomposition. The key idea of the extension is to convert a complex-valued univariate signal into a longer real-valued signal by augmenting the real part with the flipped imaginary part, and then to decompose it into intrinsic mode functions (IMFs) using the EMD once only. The bivariate IMFs are then retrieved from the obtained IMFs. Their empirical results on synthetic data show that the proposed method significantly outperforms the traditional bivariate EMD (BEMD) method in terms of computational efficiency while producing a comparable extraction error. Moreover, the proposed method shows better micro-Doppler signature analysis performance on physically measured continuous-wave radar data than that of the BEMD.