Multi-scale spectral feature extraction for underwater acoustic target recognition

Jiang, Junjun; Shi, Tuo; Huang, Min; Xiao, Zhongzhe

doi:10.1016/j.measurement.2020.108227

Cited by 40 publications

(14 citation statements)

References 16 publications

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“…With the increasing complexity of the marine environment, the classification and identification of underwater acoustic targets are of great importance in areas such as national defense and the exploitation of marine resources [ 1 , 2 ]. A ship-radiated noise signal (S-NS), as the focus of research in the field of underwater acoustics, contains a variety of information such as ship target type, tonnage, speed, and so on, which is helpful in the recognition, classification, and tracking of ship targets [ 3 , 4 ].…”

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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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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“…In 2017, Ferguson et al [9] used deep neural networks to identify radiated noise from vessels in shallow waters. Jiang J et al [10] proposed a target feature extraction and analysis method based on Gammatone frequency cepstrum coefficient (GFCC), which classified three types of target samples of small low-speed vessels, small high-speed vessels and large vessels in low-noise reservoir samples and high-noise offshore samples.…”

Section: Introductionmentioning

confidence: 99%

A LOFAR spectrum multi-sub-band matching method for passive target recognition

Shang²,

Chen³

et al. 2022

2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022)

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Template matching technique is a common technical tool in underwater acoustic target recognition. The traditional template matching method calculates the distance between the test sample and the template on the basis of feature extraction and compression, and characterizes the similarity by the distance measure, which requires high feature extraction and compression techniques. This paper proposes a Siamese network-based multi-sub-band matching method for the LOFAR spectrum of underwater acoustic targets from the target LOFAR waterfall spectrogram. The method fully considers the limited sample of underwater acoustic target data and the difference of energy distribution of the target in different frequency bands, divides the LOFAR spectrum into multiple sub-bands, and constructs a multi-sub-band Siamese network model under small sample conditions, which is suitable for solving the matching recognition problem of underwater acoustic targets. The actual sea trial data verified the effectiveness of the proposed method.

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“…However, the extremely complex marine environment may seriously interfere with receiving underwater acoustic signals [1][2][3][4]. Therefore, it poses challenges to feature extraction of underwater acoustic signals and GNSS positioning [5,6].…”

Section: Introductionmentioning

confidence: 99%

A comparative study of four types of multi-scale entropies in feature extraction of underwater acoustic signals for potential GNSS positioning applications

Zhao

Lei

et al. 2022

Front. Phys.

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The combination of underwater acoustic processing and the Global Navigation Satellite System (GNSS) has achieved remarkable economic benefits in offshore operations. As the key technology of GNNS positioning, feature extraction of underwater acoustic signals is affected by the complex marine environment. To extract more effective information from underwater acoustic signals, we use four types of multi-scale entropies, including multi-scale sample entropy (MSE), multi-scale fuzzy entropy (MFE), multi-scale permutation entropy (MPE), and multi-scale dispersion entropy (MDE), to analyze and distinguish underwater acoustic signals. In this study, two groups of real-word underwater acoustic signal experiments were performed for feature extraction of ship-radiated noises (SRNs) and ambient noises (ANs). The results indicated that the performance of the MFE-based feature extraction method is superior to that of feature extraction methods based on the other three entropies under the same number of features, and the highest average recognition rate (ARR) of the MFE-based feature extraction method for SRNs reaches 100% when the number of features is 3.

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Multi-scale spectral feature extraction for underwater acoustic target recognition

Cited by 40 publications

References 16 publications

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

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

A LOFAR spectrum multi-sub-band matching method for passive target recognition

A comparative study of four types of multi-scale entropies in feature extraction of underwater acoustic signals for potential GNSS positioning applications

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