Feature extraction of ship shaft electric field based on MEFD-wavelet threshold denoising

To realize feature extraction of ship shaft-rate electric field at low signal-noise ratio (SNR)，a feature extraction method based on modified empirical Fourier decomposition (MEFD)-wavelet threshold was proposed．First，the MEFD which introduced the auto regressive and moving average model (ARMA) was adopted to separate signal from noise.Then，the L2-norm was calculated to screen out the efficient information components． Finally，the following components were processed by wavelet threshold denoising．To verify the feasibility of the proposed method，the simulation signatures and ship model measured signatures were conducted．Experimental results show that the proposed method is robust to the environmental noise．The index of orthogonality is 0.001 5，and similarity index is 0.450 3 in the extracting result of 2B distance in ship model experiment，which has better performance of feature extraction than other methods．The proposed method can detect the electric field signatures in a longer distance and lay a good foundation for the subsequent analysis and application of electric field characteristics. © 2024 Huazhong University of Science and Technology. All rights reserved.