Abstract
The presence of marine ambient noise makes it difficult to extract effective features from ship-radiated noise. Traditional feature extraction methods based on the Fourier transform or wavelets are limited in such a complex ocean environment. Recently, entropy-based methods have been proven to have many advantages compared with traditional methods. In this paper, we propose a novel feature extraction method for ship-radiated noise based on hierarchical entropy (HE). Compared with the traditional entropy, namely multiscale sample entropy (MSE), which only considers information carried in the lower frequency components, HE takes into account both lower and higher frequency components of signals. We illustrate the different properties of HE and MSE by testing them on simulation signals. The results show that HE has better performance than MSE, especially when the difference in signals is mainly focused on higher frequency components. Furthermore, experiments on real-world data of five types of ship-radiated noise are conducted. A probabilistic neural network is employed to evaluate the performance of the obtained features. Results show that HE has a higher classification accuracy for the five types of ship-radiated noise compared with MSE. This indicates that the HE-based feature extraction method could be used to identify ships in the field of underwater acoustic signal processing.
Highlights
Identification and classification of marine vehicles are important in the field of underwater signal processing, as they are of great value in the military and marine economy [1,2,3,4]
Results show that hierarchical entropy (HE) has a higher classification accuracy for the five types of ship-radiated noise compared with multiscale sample entropy (MSE)
This indicates that the HE-based feature extraction method could be used to identify ships in the field of underwater acoustic signal processing
Summary
Identification and classification of marine vehicles are important in the field of underwater signal processing, as they are of great value in the military and marine economy [1,2,3,4]. To distinguish different kinds of pathological signals and calculate the complexity of interested signals more accurately, multiscale sample entropy (MSE) based on the coarse-graining process [15,16,17] and hierarchical entropy (HE) based on hierarchical decomposition [18,19] have been proposed. Many previous research works applied the coarse-graining process to entropy This improvement can describe the complexity of signals at different scales. All of the above studies have proven that entropy based on multiple scales has certain applicability in feature extraction of underwater acoustic signals They did not consider the high-frequency components in the signal. HE is used as a novel feature extraction method for ship-radiated noise It has great advantages compared with methods such as MSE, preserving both the low-frequency and high-frequency components of the signal while performing multi-scale decomposition and calculating the complexity of the signals of interest.
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