A New Ship-Radiated Noise Feature Extraction Technique Based on Variational Mode Decomposition and Fluctuation-Based Dispersion Entropy. [PDF]
Entropy (Basel), 2019 Sea environment complexity and underwater acoustic channels make it hard to extract features of ship-radiated noise signals. This paper presents a novel feature extraction method using the advantages of variational mode decomposition (VMD), fluctuation ...
Yang H, Zhao K, Li G.
europepmc +2 more sources
Hierarchical Cosine Similarity Entropy for Feature Extraction of Ship-Radiated Noise. [PDF]
Entropy (Basel), 2018 The classification performance of passive sonar can be improved by extracting the features of ship-radiated noise. Traditional feature extraction methods neglect the nonlinear features in ship-radiated noise, such as entropy.
Chen Z, Li Y, Liang H, Yu J.
europepmc +2 more sources
Resonance-Based Time-Frequency Manifold for Feature Extraction of Ship-Radiated Noise. [PDF]
Sensors (Basel), 2018 In this paper, a novel time-frequency signature using resonance-based sparse signal decomposition (RSSD), phase space reconstruction (PSR), time-frequency distribution (TFD) and manifold learning is proposed for feature extraction of ship-radiated noise,
Yan J +4 more
europepmc +2 more sources
A Novel Denoising Method for Ship-Radiated Noise
Journal of Marine Science and Engineering, 2023 Ship-radiated noise (SN) is one of the most critical signals in the complex marine environment; however, it is inevitably contaminated by the marine environment’s noise as well as noise from other equipment.
Yuxing Li, Chunli Zhang, Yuhan Zhou
doaj +2 more sources
Feature Extraction of Ship-Radiated Noise Based on Regenerated Phase-Shifted Sinusoid-Assisted EMD, Mutual Information, and Differential Symbolic Entropy. [PDF]
Entropy (Basel), 2019 To improve the recognition accuracy of ship-radiated noise, a feature extraction method based on regenerated phase-shifted sinusoid-assisted empirical mode decomposition (RPSEMD), mutual information (MI), and differential symbolic entropy (DSE) is ...
Li G, Yang Z, Yang H.
europepmc +2 more sources
Feature Extraction of Ship-Radiated Noise Based on Intrinsic Time-Scale Decomposition and a Statistical Complexity Measure [PDF]
Entropy (Basel), 2019 Extracting effective features from ship-radiated noise is an important way to improve the detection and recognition performance of passive sonar. Complexity features of ship-radiated noise have attracted increasing amounts of attention.
Junxiong Wang, Zhe Chen
europepmc +2 more sources
Entropy, 2017 In view of the problem that the features of ship-radiated noise are difficult to extract and inaccurate, a novel method based on variational mode decomposition (VMD), multi-scale permutation entropy (MPE) and a support vector machine (SVM) is proposed to
Yuxing Li, Yaan Li, Jing Yu
exaly +3 more sources
Analysis of the Underwater Radiated Noise Generated by Hull Vibrations of the Ships
Sensors, 2023 Shipping traffic is recognised as the main man-noise source of the anthropogenic noise generated in the marine environment. The underwater acoustic pollution is increased due to the increment of the human activity at seas supposing a threat for marine ...
Rodrigo F. Javier +4 more
doaj +4 more sources
Multi-Stage Feature Extraction and Classification for Ship-Radiated Noise. [PDF]
Sensors (Basel), 2021 Due to the complexity and unique features of the hydroacoustic channel, ship-radiated noise (SRN) detected using a passive sonar tends mostly to distort. SRN feature extraction has been proposed to improve the detected passive sonar signal. Unfortunately, the current methods used in SRN feature extraction have many shortcomings.
Esmaiel H +5 more
europepmc +5 more sources
A Novel Underwater Acoustic Target Recognition Method Based on MFCC and RACNN. [PDF]
Sensors (Basel)In ocean remote sensing missions, recognizing an underwater acoustic target is a crucial technology for conducting marine biological surveys, ocean explorations, and other scientific activities that take place in water.
Liu D, Yang H, Hou W, Wang B.
europepmc +2 more sources