Adaptive Magnetic Anomaly Detection Method with Ensemble Empirical Mode Decomposition and Minimum Entropy Feature

Abstract

Due to the fast attenuation of the magnetic field along with the distance, the magnetic anomaly generated by the remote magnetic target is usually buried in the magnetic noise. In order to improve the performance of magnetic anomaly detection (MAD) with low SNR, we propose an adaptive method of MAD with ensemble empirical mode decomposition (EEMD) and minimum entropy (ME) feature. The magnetic data is decomposed into the multiple intrinsic modal functions (IMFs) with different scales by EEMD. According to a defined criterion, the magnetic noise and magnetic signal are reconstructed based on IMFs, respectively. Entropy feature of reconstructed magnetic signal is extracted based on the probability density function (PDF) of the noise which is updated by the reconstructed magnetic noise. Compared to the traditional minimum entropy method, the entropy feature extracted by the proposed method is more obvious. The magnetic anomaly is detected whenever the entropy feature drops below the threshold. Thus, it is effective for revealing the weak magnetic anomaly by the proposed method. The measured magnetic noise is used to validate the performance of the proposed method. The results show that the detection probability of the proposed method is higher with low input SNR.

Active Electric Anomaly Detection Method for Underwater Targets Based on the Orthonormal Basis Function

“…Thus, the electric anomaly signal was completely submerged in the noise. The canonical anomaly detection techniques, including the minimum information entropy (MED) [22,23] and empirical mode decomposition (EMD) [24], were used for comparison. The result obtained by the proposed AEAD processing method is shown by the red line in Figure 6, where the target signal can be clearly revealed.…”

Combined EEMD with a Novel Flexible Wavelet Threshold Function for Weighing Signal Denoising Approach

“…Time domain analysis methods have both time and frequency domain information and can obtain better performance; for example, Lee et al [20] used the time-frequency reflection method for cable fault detection to obtain higher accuracy and a wider range of adaptation scenarios. Many scholars have also proposed improved methods related to EMD and WT to adapt to the complexity of signal and the diversity of applications, such as ensemble empirical mode decomposition (EEMD) [21] and variational mode decomposition (VMD) [16,22]. There are many ways to improve wavelet denoising methods, such as improving the threshold function to solve the problem that reconstruction signal may oscillate or distort that caused by traditional threshold functions [23][24][25][26][27][28][29][30][31], setting adaptive threshold [32,33], finding the optimal wavelet basis [34][35][36], and setting self-adaptive wavelet decomposition level [15]; some scholars combine EMD and WT [37].…”