Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition
Joint Authors
Cheng, En
Sun, Haixin
Yan, Jiaquan
Kuai, Xiaoyan
Zhang, Xiaoliang
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-05-17
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Under the complex oceanic environment, robust and effective feature extraction is the key issue of ship radiated noise recognition.
Since traditional feature extraction methods are susceptible to the inevitable environmental noise, the type of vessels, and the speed of ships, the recognition accuracy will degrade significantly.
Hence, we propose a robust time-frequency analysis method which combines resonance-based sparse signal decomposition (RSSD) and Hilbert marginal spectrum (HMS) analysis.
First, the observed signals are decomposed into high resonance component, low resonance component, and residual component by RSSD, which is a nonlinear signal analysis method based not on frequency or scale but on resonance.
High resonance component is multiple simultaneous sustained oscillations, low resonance component is nonoscillatory transients, and residual component is white Gaussian noises.
According to the low-frequency periodic oscillatory characteristic of ship radiated noise, high resonance component is the purified ship radiated noise.
RSSD is suited to noise suppression for low-frequency oscillation signals.
Second, HMS of high resonance component is extracted by Hilbert-Huang transform (HHT) as the feature vector.
Finally, support vector machine (SVM) is adopted as a classifier.
Real audio recordings are employed in the experiments under different signal-to-noise ratios (SNRs).
The experimental results indicate that the proposed method has a better recognition performance than the traditional method under different SNRs.
American Psychological Association (APA)
Yan, Jiaquan& Sun, Haixin& Cheng, En& Kuai, Xiaoyan& Zhang, Xiaoliang. 2017. Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition. Shock and Vibration،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1204862
Modern Language Association (MLA)
Yan, Jiaquan…[et al.]. Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition. Shock and Vibration No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1204862
American Medical Association (AMA)
Yan, Jiaquan& Sun, Haixin& Cheng, En& Kuai, Xiaoyan& Zhang, Xiaoliang. Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition. Shock and Vibration. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1204862
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1204862