Power frequency magnetic anomaly signal denoising algorithm based on PSO-VMD

In order to solve the problem that it is difficult to extract the characteristics of power frequency underwater magnetic target signal under strong background noise interference，a noise reduction method based on particle swarm optimization (PSO) optimized variational mode decomposition (VMD) was proposed．When optimizing VMD，the envelope spectrum peak factor was selected as the fitness function． This algorithm can not only effectively overcome the modal aliasing and endpoint effect of the empirical mode decomposition (EMD) algorithm，but also overcome the problem that VMD relies on artificial experience to adjust the parameters，resulting in a deviation in the decomposition effect．It was applied to the noise reduction examples of simulated and measured signals．The results show that compared with the ensemble empirical mode decomposition (EEMD) and VMD algorithm，the PSO-VMD algorithm not only improves the signal-to-noise ratio by about 22 dB，but also retains the original characteristics of the magnetic anomaly signal to the maximum extent．The magnetic disturbance signal of the underwater target is extracted，which provides a new idea for underwater magnetic anomaly detection． © 2024 Huazhong University of Science and Technology. All rights reserved.