An Effective Source Number Enumeration Approach Based on SEMD

Abstract

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 nonstationary 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. 17 18 if the number of signal sources is wrong. Therefore, the 48 estimation of the number of signal sources is the primary task 49 of DOA estimation. 50 Early methods of estimating the number of signal sources 51 were based on the method of hypothesis testing, but these 52 methods may be affected by subjective behavior during man-53 ual settings. In order to avoid this problem, later researchers 54 proposed estimation methods based on information theoretic 55 criteria (ITC). Akaike information criterion (AIC) proposed 56 by Wax and Kailath [11] is a representative of these algo-57 rithms. The AIC method has good estimation performance 58 under a low signal-to-noise ratio (SNR), but the method is 59 not consistent in estimation and performs poorly at high SNR 60 and a large number of snapshots. Minimum description length 61 (MDL) proposed by Wax and Ziskind [12] achieved consis-62 tent estimation, but it performs pooly at low SNR and a small 63 number of snapshots. Later, researchers improved the source 64 number estimation method based on ITC Guo et al. [13] pro-65 posed a new source number estimation method based on 66 MDL, which uses the modified covariance matrix and its 67 eigenvectors to replace the eigenvalues used to estimate the 68 source number. A new decision variable with better anti-noise 69 ability is obtained by a series of transformations on the snap-70 shot vector and the feature vector. Experiment shows that 71 this metho...

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“…The original version of the mode decomposition algorithm is the EMD proposed by Huang et al (1998). However, the EMD algorithm suffers from end effects and mode mixing (Ge et al, 2022). In order to solve the mode mixing problem, the Ensemble Empirical Mode Decomposition (EEMD) (Huang et al, 1999) and the Complementary Ensemble Empirical Mode Decomposition (CEEMD) (Yeh et al, 2010) have been proposed, both of which change the distribution of extreme points by introducing white noise, thus reducing the mode mixing.…”