A Complexity-Based Approach for the Detection of Weak Signals in Ocean Ambient Noise

Siddagangaiah, Shashidhar; Li, Yaan; Guo, Xijing; Chen, Xiao; Zhang, Qunfei; Yang, Kunde; Yang, Yixin

doi:10.3390/e18030101

Cited by 55 publications

(20 citation statements)

References 36 publications

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“…At the same time, SR-N signals are the basis for the development, testing and use of these naval equipment. Due to the marine environmental noise and the time-varying characteristics of the underwater acoustic channel, the measured SR-N signal is a nonlinear and non-stationary chaotic signal [5,6]. Noise reduction of SR-N signal is beneficial to further feature extraction, classification and detection [7].…”

Section: Introductionmentioning

confidence: 99%

A Denoising Method of Ship Radiated Noise Signal Based on Modified CEEMDAN, Dispersion Entropy, and Interval Thresholding

Yang

2019

Electronics

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Due to the non-linear and non-stationary characteristics of ship radiated noise (SR-N) signal, the traditional linear and frequency-domain denoising methods cannot be used for such signals. In this paper, an SR-N signal denoising method based on modified complete ensemble empirical mode decomposition (EMD) with adaptive noise (CEEMDAN), dispersion entropy (DE), and interval thresholding is proposed. The proposed denoising method has the following advantages: (1) as an improved version of CEEMDAN, modified CEEMDAN (MCEEMDAN) combines the advantages of EMD and CEEMDAN, and it is more reliable than CEEMDAN and has less consuming time; (2) as a fast complexity measurement technology, DE can effectively identify the type of intrinsic mode function (IMF); and (3) interval thresholding is used for SR-N signal denoising, which avoids loss of amplitude information compared with traditional denoising methods. Firstly, the original signal is decomposed into a series of IMFs using MCEEMDAN. According to the DE value of IMF, the modes are divided into three types: noise IMF, noise-dominated IMF and pure IMF. After noise IMFs are removed, the noise-dominated IMFs are denoised using interval thresholding. Finally, the pure IMF and the processed noise-dominated IMFs are reconstructed to obtain the final denoised signal. The denoising experiments with the Chen's chaotic system show that the proposed method has a higher signal-to-noise ratio (SNR) than the other three methods. Applying the proposed method to denoise the real SR-N signal, the topological structure of chaotic attractor can be recovered clearly. It is proved that the proposed method can effectively suppress the high-frequency noise of SR-N signal.Empirical mode decomposition (EMD) [8] is an adaptive signal decomposition method, which can decompose the signal into a series of intrinsic mode functions (IMFs). Therefore, EMD provides a new idea for nonlinear and non-stationary signal processing, but EMD has problems such as the mode mixing and residual noise. In order to alleviate the above problems, some noise-assisted versions have been proposed, such as ensemble EMD (EEMD) [9], complementary EEMD (CEEMD) [10] and complete EEMD with adaptive noise (CEEMDAN) [11]. These optimized EMD algorithms are widely used in various fields, especially in medicine [12,13], fault diagnosis [14,15], chaotic signal processing [16], wind power prediction [17] and so on.CEEMDAN can overcome the mode mixing of EMD and reduce the reconstruction error of EEMD. However, each time an IMF is extracted, white Gaussian noise is added multiple times in the signal, which increases the algorithm complexity. In fact, in the decomposition process, the high-frequency intermittent components and noises that cause the mode mixing are usually decomposed first [18]. After separating these components, the extreme values of the remaining signal are evenly distributed [19]. Therefore, there is no need to use white Gaussian noise as an auxiliary for residual signal, the EMD decomposition can be directly perf...

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Section: Introductionmentioning

confidence: 99%

A Denoising Method of Ship Radiated Noise Signal Based on Modified CEEMDAN, Dispersion Entropy, and Interval Thresholding

Yang

2019

Electronics

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“…SN signal contains more characteristic parameters of ship, which is an important indicator of the performance of ship. Therefore, the denoising and feature extraction for SN are critical technologies in the underwater acoustic field [1]. Due to the presence of noise, the physical characteristics of real signal are covered up.…”

Section: Introductionmentioning

confidence: 99%

Denoising and Feature Extraction Algorithms Using NPE Combined with VMD and Their Applications in Ship-Radiated Noise

Xiao

et al. 2017

Symmetry

Self Cite

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Abstract:A new denoising algorithm and feature extraction algorithm that combine a new kind of permutation entropy (NPE) and variational mode decomposition (VMD) are put forward in this paper. VMD is a new self-adaptive signal processing algorithm, which is more robust to sampling and noise, and also can overcome the problem of mode mixing in empirical mode decomposition (EMD) and ensemble EMD (EEMD). Permutation entropy (PE), as a nonlinear dynamics parameter, is a powerful tool that can describe the complexity of a time series. NPE, a new version of PE, is interpreted as distance to white noise, which shows a reverse trend to PE and has better stability than PE. In this paper, three kinds of ship-radiated noise (SN) signal are decomposed by VMD algorithm, and a series of intrinsic mode functions (IMF) are obtained. The NPEs of all the IMFs are calculated, the noise IMFs are screened out according to the value of NPE, and the process of denoising can be realized by reconstructing the rest of IMFs. Then the reconstructed SN signal is decomposed by VMD algorithm again, and one IMF containing the most dominant information is chosen to represent the original SN signal. Finally, NPE of the chosen IMF is calculated as a new complexity feature, which constitutes the input of the support vector machine (SVM) for pattern recognition of SN. Compared with the existing denoising algorithms and feature extraction algorithms, the effectiveness of proposed algorithms is validated using the numerical simulation signal and the different kinds of SN signal.

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“…This condition is caused by the broadband signal and its interaction with the measured element. Several related papers [4][5][6][7][8][9] introduce and discuss various applications of noise spectroscopy, utilizing tests of periodic materials and metamaterials. The research performed to date has nevertheless shown that, for evaluating and comparing the quantitative values monitored during the assessment of properties of periodic structures via noise spectroscopy, the approach based on the description of the frequency spectrum is considerably ineffective and unclear.…”

Section: Introductionmentioning

confidence: 99%

“…In this sense, from a broader perspective, an interesting attempt to challenge traditional detection concepts was proposed by Yang et al, whose algorithms and theorems outlined within the above-referenced paper [7] may embody a viable solution to some of the drawbacks that affect current procedures within signal detection and noise processing. Certain fields and disciplines, then, utilize substantially simplified theoretical tools [6] or analyses based on decomposing the resulting signal into elements of infinite or wide series [7]; similarly, stochastic instruments are also employed, complementing the two previously mentioned approaches that enable the description of large-scale systems as a nondeterministic model of research [8]. In such solutions, it is usually suitable to consider the origin and source of the ultra-wideband (UWB) signal.…”

Section: Introductionmentioning

confidence: 99%

The Evaluation of Noise Spectroscopy Tests

Fiala

Drexler

Nespor

et al. 2016

Entropy

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Abstract:The paper discusses mathematical tools to evaluate novel noise spectroscopy based analysis and describes, via physical similarity, the mathematical models expressing the quantitative character of the modeled task. Using the Stefan-Boltzmann law, the authors indicate finding the spectral density of the radiated power of a hemisphere, and, for the selected frequency interval and temperature, they compare the simplified models with the expression of noise spectral density according to the Johnson-Nyquist formula or Nyquist's expression of the function of spectral density based on a derivation of Planck's law. The related measurements and evaluations, together with analyses of the noise spectroscopy of periodic resonant structures, are also outlined in the given context.

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A Complexity-Based Approach for the Detection of Weak Signals in Ocean Ambient Noise

Cited by 55 publications

References 36 publications

A Denoising Method of Ship Radiated Noise Signal Based on Modified CEEMDAN, Dispersion Entropy, and Interval Thresholding

A Denoising Method of Ship Radiated Noise Signal Based on Modified CEEMDAN, Dispersion Entropy, and Interval Thresholding

Denoising and Feature Extraction Algorithms Using NPE Combined with VMD and Their Applications in Ship-Radiated Noise

The Evaluation of Noise Spectroscopy Tests

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