Next Article in Journal
The Introduction of Entropy and Information Methods to Ecology by Ramon Margalef
Previous Article in Journal
Model of Random Field with Piece-Constant Values and Sampling-Restoration Algorithm of Its Realizations
Previous Article in Special Issue
Automatic Modulation Classification of Digital Communication Signals Using SVM Based on Hybrid Features, Cyclostationary, and Information Entropy
Open AccessArticle

A Comparative Study of Multiscale Sample Entropy and Hierarchical Entropy and Its Application in Feature Extraction for Ship-Radiated Noise

1, 1,2,* and 1,*
1
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
2
Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry of Information Technology, Xi’an 710072, China
*
Authors to whom correspondence should be addressed.
Entropy 2019, 21(8), 793; https://doi.org/10.3390/e21080793
Received: 2 July 2019 / Revised: 8 August 2019 / Accepted: 13 August 2019 / Published: 14 August 2019
(This article belongs to the Special Issue Entropy and Information Theory in Acoustics)
  |  
PDF [1061 KB, uploaded 14 August 2019]
  |  

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. View Full-Text
Keywords: underwater signal processing; feature extraction; multiscale sample entropy (MSE); hierarchical entropy (HE); ship-radiated noise underwater signal processing; feature extraction; multiscale sample entropy (MSE); hierarchical entropy (HE); ship-radiated noise
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Li, W.; Shen, X.; Li, Y. A Comparative Study of Multiscale Sample Entropy and Hierarchical Entropy and Its Application in Feature Extraction for Ship-Radiated Noise. Entropy 2019, 21, 793.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top