A Novel Source Number Enumeration Approach Based on Ensemble Learning and SEEMD

Empirical Mode Decomposition (EMD) is a widely adopted adaptive signal analysis method, particularly effective for source number estimation in non-stationary environments. It decomposes complex signals into a series of Intrinsic Mode Functions (IMFs) and a residual, thereby facilitating the capture of localized time-frequency characteristics. However, standard EMD is prone to limitations such as mode mixing and end effects, which degrade decomposition fidelity and impair estimation accuracy. To overcome these shortcomings, this study proposes Supplementary Enhanced Empirical Mode Decomposition (SEEMD), an improved variant of EMD designed to enhance decomposition robustness. By injecting controlled white noise and extending signal boundaries, SEEMD significantly mitigates end effects and mode mixing, leading to more reliable IMF extraction. Following decomposition, the Hilbert Transform (HT) is applied to derive the instantaneous phase of each IMF, which serves as the basis for feature extraction. These features are subsequently fed into an ensemble learning framework for robust source number estimation. Experimental results verify that the proposed SEEMD-based approach, when integrated with ensemble learning, out-performs conventional methods, especially under low signal-to-noise ratio (SNR) and limited snapshot conditions. Moreover, the method demonstrates strong adaptability and resilience in real-world signal environments.