Underwater bottom still mine classification using robust time–frequency feature and relevance vector machine

Wang, Qiang

doi:10.1080/00207160701704572

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…Sparse signal representation, morphological component analysis (MCA), and a tunable Q-factor wavelet transform (TQWT) are adopted in the RSSD algorithm [11]. TQWT is applied to acquire the basic functions of high-Q transform and low-Q transform and obtain the corresponding transform coefficients for signal decomposition [21].…”

Section: Resonance-based Sparse Signal Decompositionmentioning

confidence: 99%

“…But it is well known that timefrequency domain analysis has much more advantages for ship radiated noises which are nonstationary signals. The method that combined the short-time Fourier transform (STFT) and relevance vector machine (RVM) was described in [11]. The methods based on wavelet transform for underwater target classification were presented in [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition

Yan

Sun

Cheng

et al. 2017

Shock and Vibration

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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.

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Section: Resonance-based Sparse Signal Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition

Yan

Sun

Cheng

et al. 2017

Shock and Vibration

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“…At present, traditional feature extraction methods mainly include frequency domain and time domain feature extraction methods [ 4 , 5 , 6 ], which can only effectively extract linear and stationary signals. However, MBN is a classic underwater acoustic signal with nonlinear, nonstationary, and non-Gaussian characteristics [ 7 ], and traditional feature extraction methods cannot effectively reflect its information [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Feature Extraction of Marine Background Noise Based on Nonlinear Dynamic Features

Ji¹,

Wang²,

Wang³

2023

Entropy

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Marine background noise (MBN) is the background noise of the marine environment, which can be used to invert the parameters of the marine environment. However, due to the complexity of the marine environment, it is difficult to extract the features of the MBN. In this paper, we study the feature extraction method of MBN based on nonlinear dynamics features, where the nonlinear dynamical features include two main categories: entropy and Lempel–Ziv complexity (LZC). We have performed single feature and multiple feature comparative experiments on feature extraction based on entropy and LZC, respectively: for entropy-based feature extraction experiments, we compared feature extraction methods based on dispersion entropy (DE), permutation entropy (PE), fuzzy entropy (FE), and sample entropy (SE); for LZC-based feature extraction experiments, we compared feature extraction methods based on LZC, dispersion LZC (DLZC) and permutation LZC (PLZC), and dispersion entropy-based LZC (DELZC). The simulation experiments prove that all kinds of nonlinear dynamics features can effectively detect the change of time series complexity, and the actual experimental results show that regardless of the entropy-based feature extraction method or LZC-based feature extraction method, they both present better feature extraction performance for MBN.

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“…The oscillation nature, duffing oscillator [ 10 ] and stochastic resonance theory [ 11 ] are utilized to detect the line-spectrum of ship-radiated noise. According to their non-stationary nature, emerged time-frequency analysis techniques are much more suitable for non-stationary signals for combining the advantages of methods that provide the non-stationary information in the time domain and frequency domain, such as the short-time Fourier transform (STFT) [ 12 , 13 ], wavelet transform (WT) [ 7 , 14 ] and the Hilbert–Huang transform (HHT) [ 15 , 16 ]. Taking into consideration the non-linear nature of ship-radiated noise, numerous methods are employed for non-linear feature extraction, including phase space reconstruction [ 17 , 18 ], fractal-based approaches [ 19 , 20 ] and complexity measures [ 21 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Resonance-Based Time-Frequency Manifold for Feature Extraction of Ship-Radiated Noise

Yan

Sun

Chen

et al. 2018

Sensors

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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, which is called resonance-based time-frequency manifold (RTFM). This is suitable for analyzing signals with oscillatory, non-stationary and non-linear characteristics in a situation of serious noise pollution. Unlike the traditional methods which are sensitive to noise and just consider one side of oscillatory, non-stationary and non-linear characteristics, the proposed RTFM can provide the intact feature signature of all these characteristics in the form of a time-frequency signature by the following steps: first, RSSD is employed on the raw signal to extract the high-oscillatory component and abandon the low-oscillatory component. Second, PSR is performed on the high-oscillatory component to map the one-dimensional signal to the high-dimensional phase space. Third, TFD is employed to reveal non-stationary information in the phase space. Finally, manifold learning is applied to the TFDs to fetch the intrinsic non-linear manifold. A proportional addition of the top two RTFMs is adopted to produce the improved RTFM signature. All of the case studies are validated on real audio recordings of ship-radiated noise. Case studies of ship-radiated noise on different datasets and various degrees of noise pollution manifest the effectiveness and robustness of the proposed method.

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Underwater bottom still mine classification using robust time–frequency feature and relevance vector machine

Cited by 7 publications

References 21 publications

Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition

Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition

Comparative Study on Feature Extraction of Marine Background Noise Based on Nonlinear Dynamic Features

Resonance-Based Time-Frequency Manifold for Feature Extraction of Ship-Radiated Noise

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