Abstract
Ship-radiated noise signal has a lot of nonlinear, non-Gaussian, and nonstationary information characteristics, which can reflect the important signs of ship performance. This paper proposes a novel feature extraction technique 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. First, IITD is applied to decompose the ship-radiated noise signal into a series of intrinsic scale components (ISCs). Then, we select the ISC with the main information through the correlation analysis, and calculate the MDE value as feature vectors. Finally, the feature vectors are input into the support vector machine (SVM) for ship classification. The experimental results indicate that the recognition rate of the proposed technique reaches 86% accuracy. Therefore, compared with the other feature extraction methods, the proposed method provides a new solution for classifying different types of ships effectively.
Highlights
During the development of passive sonar, the ship-radiated noise signal has been widely used in the detection, tracking, and classification of ship targets
The results demonstrate that the intrinsic time-scale decomposition (IITD)-multiscale dispersion entropy (MDE) value is at same level for the same ships, but there is an obvious difference for different types of ships
We carried out an investigation aimed at gaining a better recognition accuracy of ship-radiated noise feature extraction aimed methodat based on IITD-MDE
Summary
During the development of passive sonar, the ship-radiated noise signal has been widely used in the detection, tracking, and classification of ship targets. We used akima interpolation [25] to improve the ITD method; the IITD algorithm was proposed This is a feasible way to decompose the ship-radiated noise signal by IITD to extract effective ISCs. Entropy theory can efficiently evaluate the complexity of the time series and reduce the dimension of the feature vector and fully represent the characteristics of the series. The coarse-graining process has better stability in feature extraction and can be combined with arbitrary entropy estimators for multiscale analysis Regarding this advantage, a multiscale dispersion entropy (MDE) procedure was put forward to estimate the complexity of the original time series over a range of scales [33].
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