Entropy feature analysis is an important tool for classification and identification of different types of ships. In order to improve the limitations of traditional feature extraction of shipradiation noise in complex marine environments, we proposed a novel feature extraction method for ship-radiated noise based on improved intrinsic time-scale decomposition (IITD) and multiscale dispersion entropy (MDE). The proposed feature extraction technique, named IITD-MDE. IITD as an improved algorithm has more reliable performance than instrinsic time-scale decomposition(ITD). Firstly, five types of ship-radiated noise signals are decomposed into a series of intrinsic scale component (ISCs) by IITD. Then, we select the ISC with main information through the correlation analysis, and calculate the MDE value as feature vector. Finally, input the feature vector into the support vector machine (SVM) classifier to analysis and get classification. The experimental results demonstrate that the recognition rate of the proposed technique reaches 86% of accuracy. Therefore, compare with the other feature extraction methods, the proposed method is able to classify the different types of ships effectively.
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A Novel Improved Feature Extraction Technique for Ship-radiated Noise Based on Improved Intrinsic Time-scale Decomposition and Multiscale Dispersion Entropy
Published: 17 November 2019 by MDPI in 5th International Electronic Conference on Entropy and Its Applications session Information Theory, Probability, Statistics, and Artificial Intelligence
Keywords: ship-radiated noise; dispersion entropy (DE); multiscale dispersion entropy (MDE); instrinsic time-scale decomposition (ITD); improved instrinsic time-scale decomposition (IITD); instrinsic scale component(ISC); feature extraction