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.
Highlights
Information technology is developing rapidly nowadays, and it is applied in various fields
We propose a single feature extraction method based on Slope entropy (SlEn) and a double feature extraction method based on SlEn combined with permutation entropy (SlEn&permutation entropy (PE))
It is shown that the classification of SlEn&PE for the four types of ship-radiated noise signal (SNS) samples has the highest average recognition rate, having only one wrongly classified sample in the population, which is 4.22% higher than the proposed single feature extraction method
Summary
Information technology is developing rapidly nowadays, and it is applied in various fields. In 2019, David Cuesta-Frau [36] proposed a new entropy estimator termed Slope entropy (SlEn), which is based on the relative frequency of simple symbol patterns She used SlEn to extract electroencephalographic (EEG) signals compared with PE and SE, and the results show that SlEn has the best classification effect. Cuesta-Frau et al [38] designed a study based on SlEn to compare dynamic recordings from internal emotional outburst symptoms of long follow-up patients with bipolar disorder (BD), and the results proved that SlEn is practicable for distinguishing between depression and mania episodes These papers prove that SlEn is an entropy estimator with a good classification effect.
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