LAUSR

In signal processing, empirical mode decomposition (EMD) first decomposes the received single-channel signal into several intrinsic mode functions (IMFs) and a residual, and then uses machine learning methods for source number enumeration. EMD, however, has an end effect that can undermine the accuracy of source number enumeration. To address this issue, this paper proposed a new EMD method named Supplementary Empirical Mode Decomposition (SEMD), which improved the accuracy by extending the signal length. The proposed method can be better applied to the modal parameter identification of non-stationary and nonlinear data in the engineering field. This method first identifies two candidate extreme points, which are the closest to the function value of the first extreme point near the endpoint. Then, on one side of the candidate point, it finds a waveform similar to that at the endpoint. Finally, the maximum and minimum points at each end of the signal will be added to extend the length of the signal. The added extreme points are candidate extreme points in similar waveforms. For the improved source number enumeration method based on SEMD, the instantaneous phase is obtained first by SEMD and Hilbert transform (HT). Then, the instantaneous phase feature is extracted to obtain a high-dimensional eigenvalue vector. Finally, the back propagation (BP) neural network is used to predict the number of sources. Experiment shows that SEMD can effectively restrain the end effect, and the source number enumeration algorithm based on SEMD has a higher correct detection probability than others.