Abstract
Entropy feature analysis is an important tool for the classification and identification of different types of ships. In order to improve the limitations of traditional feature extraction of ship-radiation 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 is named IITD-MDE. IITD, as an improved algorithm, has more reliable performance than intrinsic 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 the main information through correlation analysis, and calculate the MDE value as a feature vector. Finally, the feature vector is input into the support vector machine (SVM) classifier to analyze and get classification. The experimental results demonstrate that the recognition rate of the proposed technique reaches 86% accuracy. Therefore, compared with the other feature extraction methods, the proposed method is able to classify the different types of ships effectively.
Highlights
Ship-radiated noise signal has always been the active research in the field of underwater acoustic signal processing
Akima interpolation is used, it is different from the envelop mean based on local extrema in empirical mode decomposition (EMD), because intrinsic time-scale decomposition (IITD) only requires one akima interpolation per decomposition
To improve the recognition accuracy of ship-radiated noise signals, a novel feature extraction method based on IITD and multiscale dispersion entropy (MDE) was proposed
Summary
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China Department of Computer and Information of Science and Engineering, University of Florida, Gainesville, FL 32611, USA 2019; Available online: https://ecea-5.sciforum.net/.
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