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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-based dispersion entropy (FDE) and self-organizing feature map (SOM). Firstly, VMD decomposition...
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Context 1
... order to further compare the three modal decompositions, the error between the components of the analog signal and the corresponding IMF is calculated. Table 3 shows that y1, y2, and y3 correspond to IMF1, IMF2, and IMF3 after the VMD decomposition. At this time, error is the smallest and decomposition effect is the best. ...
Context 2
... order to further compare the three modal decompositions, the error between the components of the analog signal and the corresponding IMF is calculated. Table 3 shows that 1 y , 2 y , and 3 y correspond to IMF1, IMF2, and IMF3 after the VMD decomposition. At this time, error is the smallest and decomposition effect is the best. ...
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... However, EMD suffers from mode mixing and end effects, which lead to low accuracy of the decomposed signal. To further improve the decomposition accuracy, Yang [15] and Li [16], respectively, proposed combining variational mode decomposition (VMD) with fluctuation dispersion entropy and VMD with slope entropy, achieving even higher recognition rates. However, with the emergence of new algorithms in the aforementioned methods and improving recognition rates, they also introduce a large number of redundant features and parameters, undoubtedly increasing computational costs. ...
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... Variational mode decomposition VMD first requires a constrained variational model [20]: ...
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... Variational mode decomposition VMD first requires a constrained variational model [20]: ...
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... Variational mode decomposition VMD first requires a constrained variational model [20]: ...
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... Various entropy algorithms have been proposed in the past years based on the two definitions mentioned above, such as approximate entropy (ApEn) [8], sample entropy (SampEn) [9], fuzzy entropy (FuzEn) [10], quadratic sample entropy [11], distribution entropy [12], permutation entropy (PE) [13], weighted permutation entropy (WPE) [14], modified permutation entropy [15], dispersion entropy (DispEn) [16], and fluctuation dispersion entropy (FDispEn) [17]. The development of new entropy metrics has shed new light on a wide range of engineering problems, including fault diagnosis [18,19], underwater target recognition [1,7,[20][21][22], stock market analysis [23,24], and biomedical signal processing [8,9,15,25], among others. ...
Entropy quantification approaches have gained considerable attention in engineering applications. However, certain limitations persist, including the strong dependence on parameter selection, limited discriminating power, and low robustness to noise. To alleviate these issues, this paper introduces two novel algorithms for time series analysis: the ensemble improved permutation entropy (EIPE) and multiscale EIPE (MEIPE). Our approaches employ a new symbolization process that considers both permutation relations and amplitude information. Additionally, the ensemble technique is utilized to reduce the dependence on parameter selection. We performed a comprehensive evaluation of the proposed methods using various synthetic and experimental signals. The results illustrate that EIPE is capable of distinguishing white, pink, and brown noise with a smaller number of samples compared to traditional entropy algorithms. Furthermore, EIPE displays the potential to discriminate between regular and non-regular dynamics. Notably, when compared to permutation entropy, weighted permutation entropy, and dispersion entropy, EIPE exhibits superior robustness against noise. In practical applications, such as RR interval data classification, bearing fault diagnosis, marine vessel identification, and electroencephalographic (EEG) signal classification, the proposed methods demonstrate better discriminating power compared to conventional entropy measures. These promising findings validate the effectiveness and potential of the algorithms proposed in this paper.
... Another direction is to propose a combination of decomposition methods and feature extraction methods. In [26], based on variational mode decomposition (VMD), the fluctuation-based dispersion entropy (FDE) of each IMF is differenced from the original signal, and the IMF corresponding to the minimum value is filtered out and its FDE is used as the feature value for classification. The method has a good separation effect. ...
Ship type identification is an important basis for ship management and monitoring. The paper proposed a new method of ship type identification by combining characteristic parameters from the energy difference between high and low frequencies and the sensitive IMF variance mean value based on the modal decomposition of the underwater radiated noise signals using the Ensemble Empirical Mode Decomposition (EEMD) method. The comparison shows that the characteristic parameters of different types of ship, underwater radiated noises are different, whereas those of the same types of ship, underwater radiated noises fall in close range. Validation experiments based on randomly selected ship underwater radiated noise samples manifest that the method is of good separability for the four types of ship underwater radiated noises in the Deepship dataset. It has a higher identification rate than other methods within the distance range of ship underwater radiated noise detection in the dataset. The accuracy of this method tends to decrease with distance in the classification experiments of the ship underwater radiated noises at different distances.
... signals like S-RN [16], but this excellent effect costs a lot of time; in addition, there are only a few types of fractal dimensions, such as box dimension [9,17], so the application of fractal method to extract discriminant features has not been thoroughly studied. Last but not least, for entropy, the development in feature extraction of S-RN is more comprehensive and faster than LZC and fractal dimension, and recently proposed entropy in common use are PE [18][19][20], DE [21][22][23] and slope entropy [24][25][26]. However, the PE cannot reflect the amplitude change information of S-RN, and slope entropy is seriously affected by threshold setting, and the characteristic value is sufficiently enough. ...
... The DE-based method has been widely used in feature extraction of S-RN and has shown superior performance. In this paper, we will give a comprehensive review of the DE-based methods and divide them into two types: methods that apply DE algorithms only [27,28] and methods that combine DE with mode decomposition algorithms [23,29]; then we introduce two aspects of DE in and its application for S-RN. The rest of this review is structured as follows. ...
... Improved the ITD and proposed a S-RN feature extraction method combining improved ITD (IITD) with MDE, which further enhanced the effect of feature extraction. Yang et al. [23] presented a novel a S-RN feature extraction technology using VMD and FDE, and the results presented that the presented technique has better separation effect and higher discrimination. Li et al [56]. ...
There is abundant ship information in ship-radiated noise, which is helpful for ship target recognition, classification and tracking. However, owing to the increasing complexity of the marine environment, it makes difficult to extract S-RN features. Dispersion entropy has been proven to be an excellent method to extract the features of S-RN by analyzing the complexity of S-RN, and has been widely used in feature extraction of S-RN. This paper summarizes the research progress of DE in the feature extraction of S-RN in recent years, and provides a comprehensive reference for researchers related to this topic. First, DE and its improved algorithm are described. Then the traditional and DE-based S-RN feature extraction methods are summarized, and the application of DE in S-RN feature extraction methods is concluded from two aspects: methods that apply DE algorithms only and methods that combine DE with mode decomposition algorithms. Finally, the research prospects of DE and the summary of this paper are given.
... Other researchers, in [25,26], replaced EMD with EEMD and proposed the feature extraction approaches of S-NS using sample entropy and multi-scale PE, respectively; their results show that the S-NS feature extraction approach based on EEMD had a higher recognition rate. To improve the classification performance, [27] employed CEEMDAN and energy entropy to classify the S-NSs, and the results indicated that the feature extraction approach based on CEEMDAN can accurately recognize S-NSs. In addition, Yang et al., presented a novel S-NS feature extraction approach based on VMD and FDE [28], and the experimental results showed that the feature extraction approach based on VMD is better than that based on EMD and EEMD. ...
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.
... Thus, to obtain the center frequency and bandwidth of each component, VMD constantly searches for the modes and center frequency of each mode by using an alternating direction method of a multiplier, thereby solving the variational problem. In recent years, many works tried to extend the current EMD, EEMD, and VMD methods and applied these schemes in the fields of biomedical engineering [6][7][8][9], mechanical fault diagnosis [10][11][12], and acoustic signal processing [13,14]. In [13], the VMD is firstly performed to decompose the SRN signal, and the permutation entropy (PE) of each IMF with the highest energy is then extracted, achieving a recognition rate of 94%. ...
... In [13], the VMD is firstly performed to decompose the SRN signal, and the permutation entropy (PE) of each IMF with the highest energy is then extracted, achieving a recognition rate of 94%. In [14,15] the VMD decomposition of the SRN signal was performed and the fluctuation-based dispersion entropy (FDE) of each IMF was studied; the obtained IMF with the smallest difference from the FDE of the prime signal was then chosen to describe the raw signal with a recognition rate of 97.5%. In the EMD-EIMF-PE method proposed in [16], the signal-dominant IMF by EMD was chosen based on the energy gauge, and its PE was regarded as the feature parameter effectively distributing the SRN. ...
... In the EMD-EIMF-PE method proposed in [16], the signal-dominant IMF by EMD was chosen based on the energy gauge, and its PE was regarded as the feature parameter effectively distributing the SRN. Although both [13,14] have high recognition rates, there are still flaws in these methods. The mode VMD number has specified the EMD results in [13,14], which will certainly influence the VMD decomposition accuracy. ...
Abstract: 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 pro‐posed to improve the detected passive sonar signal. Unfortunately, the current methods used in SRN feature extraction have many shortcomings. Considering this, in this paper we propose a new multi‐stage feature extraction approach to enhance the current SRN feature extractions based on enhanced variational mode decomposition (EVMD), weighted permutation entropy (WPE), local tangent space alignment (LTSA), and particle swarm optimization‐based support vector machine (PSO‐SVM). In the proposed method, first, we enhance the decomposition operation of the conventional VMD by decom‐posing the SRN signal into a finite group of intrinsic mode functions (IMFs) and then calculate the WPE of each IMF. Then, the high‐dimensional features obtained are reduced to two‐dimensional ones by using the LTSA method. Finally, the feature vectors are fed into the PSO‐SVM multi‐class classifier to realize the classification of different types of SRN sample. The simulation and experimental results demonstrate that the recognition rate of the proposed method overcomes the conventional SRN fea‐ture extraction methods, and it has a recognition rate of up to 96.6667%.
Keywords: ship‐radiated noise; variational mode decomposition; weighted permutation entropy; local tangent space alignment
... Yang et. al [34] proposed a novel feature extraction method based on variational mode decomposition and fluctuation dispersion entropy. Experimental results show that this method has higher accuracy in feature extraction of radiated noise signals from ships. ...
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.
