Feature Extraction of Ship-Radiated Noise Based on Intrinsic Time-Scale Decomposition and a Statistical Complexity Measure

Wang, Junxiong; Chen, Zhe

doi:10.3390/e21111079

Cited by 13 publications

(7 citation statements)

References 35 publications

(49 reference statements)

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“…[10] used underwater acoustic target-radiated noise signal waveforms as features to achieve effective recognition of underwater acoustic targets. Wang et al [11] proposed a new statistical complexity measure to achieve underwater target radiation noise recognition.…”

Section: Underwater Target Radiation Noise Statistical Feature Extrac...mentioning

confidence: 99%

Present status and challenges of underwater acoustic target recognition technology: A review

Lei

Xiaofang²,

Wang³

et al. 2022

Front. Phys.

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Future naval warfare has placed high demands on underwater targets’ target detection, target recognition, and opposition resistance, among other things. However, the ocean’s complex underwater acoustic environment and the evolving “stealth” technology of underwater targets pose significant challenges to target detection systems, which has become a hot topic in the field of underwater acoustic signal processing in various countries. This study introduced the mechanism of underwater target radiation noise generation, analyzed the research progress and development of underwater target radiation noise recognition by applying machine learning from three perspectives: signal acquisition, feature extraction, and signal recognition at home and abroad, and elaborated on the challenges of underwater target-radiated noise recognition technology against the backdrop of rapid computing science development, and finally, an integrated signal processing method based on the fusion of traditional feature extraction methods and deep learning is proposed for underwater target radiation noise recognition, which improves the low recognition rate of traditional methods and also circumvents the problem of deep learning requiring high computational cost, and is an important direction for future hydroacoustic signal processing.

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Section: Underwater Target Radiation Noise Statistical Feature Extrac...mentioning

confidence: 99%

Present status and challenges of underwater acoustic target recognition technology: A review

Lei

Xiaofang²,

Wang³

et al. 2022

Front. Phys.

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“…The second category is to extract the features of mode components, which are obtained with a decomposition algorithm [ 17 , 18 ]. Currently, there are many decomposition algorithms, such as empirical mode decomposition (EMD) [ 19 ], ensemble empirical mode decomposition (EEMD) [ 20 ], complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) [ 21 ], and variational mode decomposition (VMD) [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Optimized Ship-Radiated Noise Feature Extraction Approaches Based on CEEMDAN and Slope Entropy

Tang

Jiao

2022

Entropy

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Slope entropy (Slopen) has been demonstrated to be an excellent approach to extracting ship-radiated noise signals (S-NSs) features by analyzing the complexity of the signals; however, its recognition ability is limited because it extracts the features of undecomposed S-NSs. To solve this problem, in this study, we combined complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to explore the differences of Slopen between the intrinsic mode components (IMFs) of the S-NSs and proposed a single-IMF optimized feature extraction approach. Aiming to further enhance its performance, the optimized combination of dual-IMFs was selected, and a dual-IMF optimized feature extraction approach was also proposed. We conducted three experiments to demonstrate the effectiveness of CEEMDAN, Slopen, and the proposed approaches. The experimental and comparative results revealed both of the proposed single- and dual-IMF optimized feature extraction approaches based on Slopen and CEEMDAN to be more effective than the original ship signal-based and IMF-based feature extraction approaches.

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“…Experimental results show that this method has higher accuracy in feature extraction of radiated noise signals from ships. Wang et.al [35] based on spectral entropy and imbalance was proposed to reduce the impact of Marine environmental noise by using featuretime-scale decomposition. Different ships were differentiated according to the position of spectral characteristics of radiated noise in the twodimensional plane composed of Complexity and spectral entropy.…”

Section: Introuductionmentioning

confidence: 99%

Research on Feature Extraction of Ship-Radiated Noise Based on Compressed Sensing and Center Frequency

Lei

Xiaofang²,

Zhou

et al. 2021

IEEE Access

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In the field of underwater acoustic signal processing, the ship radiated noise contains a large amount of ship information, which is of great significance to the ship identification. The traditional method relies too much on the operator and prior knowledge, which seriously reduces the efficiency and accuracy of the ship radiated noise identification. This paper presented a novel ship radiated noise feature extraction method based on compression sensing and center frequency. Firstly, to compression sensing of the ship radiated noise, enhance its line spectrum energy. Then, the ship radiated noise is decomposed by empirical mode decomposition to obtain multiple intrinsic mode function, calculate the mutual information entropy of adjacent intrinsic mode function to determine the key parameter K of the variational mode decomposition. Finally, perform variational model of ship radiated noise based on K, extract the center frequency of maximum energy intrinsic mode function as the ship radiated noise recognition feature. Experimental results show that the proposed feature extraction method can classify ship radiated noise quickly and effectively, and reduce the dependence on operators and prior knowledge.

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Feature Extraction of Ship-Radiated Noise Based on Intrinsic Time-Scale Decomposition and a Statistical Complexity Measure

Cited by 13 publications

References 35 publications

Present status and challenges of underwater acoustic target recognition technology: A review

Present status and challenges of underwater acoustic target recognition technology: A review

Optimized Ship-Radiated Noise Feature Extraction Approaches Based on CEEMDAN and Slope Entropy

Research on Feature Extraction of Ship-Radiated Noise Based on Compressed Sensing and Center Frequency

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