A Novel Feature Extraction Method for Ship-Radiated Noise Based on Variational Mode Decomposition and Multi-Scale Permutation Entropy

Abstract

Abstract:In view of the problem that the features of ship-radiated noise are difficult to extract and inaccurate, a novel method based on variational mode decomposition (VMD), multi-scale permutation entropy (MPE) and a support vector machine (SVM) is proposed to extract the features of ship-radiated noise. In order to eliminate mode mixing and extract the complexity of the intrinsic mode function (IMF) accurately, VMD is employed to decompose the three types of ship-radiated noise instead of Empirical Mode Decomposition (EMD) and its extended methods. Considering the reason that the permutation entropy (PE) can quantify the complexity only in one scale, the MPE is used to extract features in different scales. In this study, three types of ship-radiated noise signals are decomposed into a set of band-limited IMFs by the VMD method, and the intensity of each IMF is calculated. Then, the IMFs with the highest energy are selected for the extraction of their MPE. By analyzing the separability of MPE at different scales, the optimal MPE of the IMF with the highest energy is regarded as the characteristic vector. Finally, the feature vectors are sent into the SVM classifier to classify and recognize different types of ships. The proposed method was applied in simulated signals and actual signals of ship-radiated noise. By comparing with the PE of the IMF with the highest energy by EMD, ensemble EMD (EEMD) and VMD, the results show that the proposed method can effectively extract the features of MPE and realize the classification and recognition for ships.

A Fusion Frequency Feature Extraction Method for Underwater Acoustic Signal Based on Variational Mode Decomposition, Duffing Chaotic Oscillator and a Kind of Permutation Entropy

“…(1) Select the low-frequency IMF for the research, such as the last IMF; (2) Obtain estimated frequency of selected IMF by VMD; (3) Detect periodic signal of selected IMF using DCO; (4) When the phase track of selected IMF is in great periodic, and the KPE of DCO system output reaches the maximum, we can determine the accurate frequency of selected IMF.…”

“…Underwater acoustic signal processing is one of the hot topics in the field of marine science. The denoising, feature extraction and classification of underwater acoustic signals are of great significance to the research of underwater acoustic signals, which can provide convenience and basis for the detection and tracking of underwater acoustic signals [1][2][3][4]. Feature extraction methods for underwater acoustic signals mainly include frequency feature extraction, energy feature extraction and complexity feature extraction [5].…”

A New Ship-Radiated Noise Feature Extraction Technique Based on Variational Mode Decomposition and Fluctuation-Based Dispersion Entropy

“…In [29], an improved decomposition method, VMD and PE are used for fault extraction. In [30], VMD, Multi-Scale Permutation Entropy (MPE) and Support Vector Machine (SVM) were used to extract the characteristics of underwater acoustic signals. The study found that although the above method continuously improves the signal decomposition effect, it does not solve the problem that PE may lose amplitude information.…”

The Data-Driven Optimization Method and Its Application in Feature Extraction of Ship-Radiated Noise with Sample Entropy

“…Due to the mode mixing of EMD, ensemble EMD [11] was put forward after EMD, which inhibits this phenomenon to some extent by adding noise. Still, EEMD and other improved EMD methods are all empirical ones without mathematical derivation [12]. In recent years, the two kinds of data-driven decomposition methods have developed rapidly.…”