ICEEMDAN–RPE–AITD algorithm for magnetic field signals of magnetic targets

• Published in: Scientific reports Vol. 15; no. 1; pp. 6509 - 15
• Main Authors: Lu, Binjie, Li, Zongji, Zhang, Xiaobing
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 22.02.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/166/987; Adaptive interval threshold denoising; Algorithms; Humanities and Social Sciences; Improved complete ensemble empirical mode decomposition with adaptive noise; Magnetic field signals; Magnetic fields; Magnetic targets; multidisciplinary; Noise; Reverse permutation entropy; Science; Science (multidisciplinary); Magnetic targets; Magnetic field signals; Improved complete ensemble empirical mode decomposition with adaptive noise; Reverse permutation entropy; Adaptive interval threshold denoising
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-91068-y

Magnetic targets magnetic field signals contain rich target feature information, but geomagnetic background noise, sensor noise and so on will cause serious interference to the targets magnetic field signals. To solve the denoising problem of magnetic targets magnetic field signals, a denoising algorithm based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), reverse permutation entropy (RPE) and adaptive interval threshold denoising (AITD), named ICEEMDAN–RPE–AITD algorithm, is proposed. The algorithm firstly adopts ICEEMDAN to decompose the signal and get the intrinsic mode functions (IMFs), then calculates the RPE value of IMFs and sets dual thresholds to classify signal IMFs (signal-dominant), noisy IMFs (mixed signal and noise), noise IMFs (noise-dominant), and finally carries out AITD on noisy IMFs, and combined the signal IMFs with the processed noisy IMFs to reconstruct the denoised signals. The denoising test is conducted using the ship model magnetic field signals, and the effectiveness of the algorithm is verified by taking the signal-to-noise ratio and the root mean square error as the evaluation indexes. Finally, the effectiveness of the algorithm is verified by using the measured magnetic field signal test of the ship, taking the noise intensity and correlation dimension as the evaluation indexes and combining with the power spectral density analysis.