Double Feature Extraction Method of Ship-Radiated Noise Signal Based on Slope Entropy and Permutation Entropy

Yuxing Li; Peiyuan Gao; Bingzhao Tang; Yingmin Yi; Jianjun Zhang

doi:10.3390/e24010022

Double Feature Extraction Method of Ship-Radiated Noise Signal Based on Slope Entropy and Permutation Entropy

Entropy (Basel). 2021 Dec 23;24(1):22. doi: 10.3390/e24010022.

Authors

Yuxing Li¹, Peiyuan Gao¹, Bingzhao Tang¹, Yingmin Yi¹, Jianjun Zhang²

Affiliations

¹ School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
² School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China.

Abstract

In order to accurately identify various types of ships and develop coastal defenses, a single feature extraction method based on slope entropy (SlEn) and a double feature extraction method based on SlEn combined with permutation entropy (SlEn&PE) are proposed. Firstly, SlEn is used for the feature extraction of ship-radiated noise signal (SNS) compared with permutation entropy (PE), dispersion entropy (DE), fluctuation dispersion entropy (FDE), and reverse dispersion entropy (RDE), so that the effectiveness of SlEn is verified, and SlEn has the highest recognition rate calculated by the k-Nearest Neighbor (KNN) algorithm. Secondly, SlEn is combined with PE, DE, FDE, and RDE, respectively, to extract the feature of SNS for a higher recognition rate, and SlEn&PE has the highest recognition rate after the calculation of the KNN algorithm. Lastly, the recognition rates of SlEn and SlEn&PE are compared, and the recognition rates of SlEn&PE are higher than SlEn by 4.22%. Therefore, the double feature extraction method proposed in this paper is more effective in the application of ship type recognition.

Keywords: dispersion entropy; feature extraction; fluctuation dispersion entropy; permutation entropy; reverse dispersion entropy; ship-radiated noise signal; slope entropy.