Feature Extraction of Ship-Radiated Noise Based on Enhanced Variational Mode Decomposition, Normalized Correlation Coefficient and Permutation Entropy

• Published in: Entropy (Basel, Switzerland) Vol. 22; no. 4; p. 468
• Main Authors: Xie, Dongri, Esmaiel, Hamada, Sun, Haixin, Qi, Jie, Qasem, Zeyad A. H.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 20.04.2020; MDPI
• Subjects: Acoustic noise; Agricultural commodities; Algorithms; Background noise; Correlation coefficients; Decomposition; enhanced variational mode decomposition; Entropy; Fault diagnosis; Feature extraction; Mathematical analysis; Noise; normalized correlation coefficient; Particle swarm optimization; permutation entropy; Permutations; ship-radiated noise; Signal processing; Support vector machines; Time series; Underwater acoustics; Wavelet transforms
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e22040468

Due to the complexity and variability of underwater acoustic channels, ship-radiated noise (SRN) detected using the passive sonar is prone to be distorted. The entropy-based feature extraction method can improve this situation, to some extent. However, it is impractical to directly extract the entropy feature for the detected SRN signals. In addition, the existing conventional methods have a lack of suitable de-noising processing under the presence of marine environmental noise. To this end, this paper proposes a novel feature extraction method based on enhanced variational mode decomposition (EVMD), normalized correlation coefficient (norCC), permutation entropy (PE), and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, EVMD is utilized to obtain a group of intrinsic mode functions (IMFs) from the SRN signals. The noise-dominant IMFs are then eliminated by a de-noising processing prior to PE calculation. Next, the correlation coefficient between each signal-dominant IMF and the raw signal and PE of each signal-dominant IMF are calculated, respectively. After this, the norCC is used to weigh the corresponding PE and the sum of these weighted PE is considered as the final feature parameter. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to classify the SRN samples. The experimental results demonstrate that the recognition rate of the proposed methodology is up to 100%, which is much higher than the currently existing methods. Hence, the method proposed in this paper is more suitable for the feature extraction of SRN signals.