Feature Extraction Of Ship-Radiated Noise Research Articles

Hierarchical Refined Composite Multi-Scale Fractal Dimension and Its Application in Feature Extraction of Ship-Radiated Noise

The fractal dimension (FD) is a classical nonlinear dynamic index that can effectively reflect the dynamic transformation of a signal. However, FD can only reflect signal information of a single scale in the whole frequency band. To solve this problem, we combine refined composite multi-scale processing with FD and propose the refined composite multi-scale FD (RCMFD), which can reflect the information of signals at a multi-scale. Furthermore, hierarchical RCMFD (HRCMFD) is proposed by introducing hierarchical analysis, which successfully represents the multi-scale information of signals in each sub-frequency band. Moreover, two ship-radiated noise (SRN) multi-feature extraction methods based on RCMFD and HRCMFD are proposed. The simulation results indicate that RCMFD and HRCMFD can effectively discriminate different simulated signals. The experimental results show that the proposed two-feature extraction methods are more effective for distinguishing six types of SRN than other feature-extraction methods. The HRCMFD-based multi-feature extraction method has the best performance, and the recognition rate reaches 99.7% under the combination of five features.

Research on Feature Extraction of Ship-Radiated Noise Based on Multiscale Fuzzy Dispersion Entropy

In recent years, fuzzy dispersion entropy (FDE) has been proposed and used in the feature extraction of various types of signals. However, FDE can only analyze a signal from a single time scale during practical application and ignores some important information. In order to overcome this drawback, on the basis of FDE, this paper introduces the concept of multiscale process and proposes multiscale FDE (MFDE), based on which an MFDE-based feature extraction method for ship-radiated noise is proposed. The experimental results of the simulated signals show that MFDE can reflect the changes in signal complexity, frequency, and amplitude, which can be applied in signal feature extraction; in addition, the measured experimental results demonstrate that the MFDE-based feature extraction method has a better feature extraction effect on ship-radiated noise, and the highest recognition rate is 99.5%, which is an improvement of 31.9% compared to the recognition rate of a single time scale. All the results show that MFDE can be better applied to the feature extraction and identification classification of ship-radiated noise.

A Fine-Grained Ship-Radiated Noise Recognition System Using Deep Hybrid Neural Networks with Multi-Scale Features

Fine-grained ship-radiated noise recognition methods of different specific ships are in demand for maritime traffic safety and general security. Due to the high background noise and complex transmission channels in the marine environment, the accurate identification of ship radiation noise becomes quite complicated. Existing ship-radiated noise-based recognition systems still have some shortcomings, such as the imperfection of ship-radiated noise feature extraction and recognition algorithms, which lead to distinguishing only the type of ships rather than identifying the specific vessel. To address these issues, we propose a fine-grained ship-radiated noise recognition system that utilizes multi-scale features from the amplitude–frequency–time domain and incorporates a multi-scale feature adaptive generalized network (MFAGNet). In the feature extraction process, to cope with highly non-stationary and non-linear noise signals, the improved Hilbert–Huang transform algorithm applies the permutation entropy-based signal decomposition to perform effective decomposition analysis. Subsequently, six learnable amplitude–time–frequency features are extracted by using six-order decomposed signals, which contain more comprehensive information on the original ship-radiated noise. In the recognition process, MFAGNet is designed by applying unique combinations of one-dimensional convolutional neural networks (1D CNN) and long short-term memory (LSTM) networks. This architecture obtains regional high-level information and aggregate temporal characteristics to enhance the capability to focus on time–frequency information. The experimental results show that MFAGNet is better than other baseline methods and achieves a total accuracy of 98.89% in recognizing 12 different specific noises from ShipsEar. Additionally, other datasets are utilized to validate the universality of the method, which achieves the classification accuracy of 98.90% in four common types of ships. Therefore, the proposed method can efficiently and accurately extract the features of ship-radiated noises. These results suggest that our proposed method, as a novel underwater acoustic recognition technology, is effective for different underwater acoustic signals.

An adaptive feature extraction technique for ship-radiated noise based on weighted multiscale mathematical morphological filtering

Aiming at the problem of feature extraction of ship-radiated noise in complex marine environment, an adaptive weighted multiscale mathematical morphological filtering method is proposed. The weight coefficients of the structure elements of different scales in multiscale morphological filtering are determined adaptively by an improved particle swarm optimization algorithm, and the Teager energy kurtosis is used as the evaluation index of the filtered optimal signal to provide optimized weights for each scale. Finally, the optimized multiscale mathematical morphology filter with selective adaptive weights is bound and applied to the feature extraction of ship-radiated noise. The analysis results of simulated signal and measured ship-radiated noise show that this method has strong noise suppression and feature extraction ability, and the calculation is simple and fast, which provides an effective method for ship feature extraction.

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

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.

A novel complexity-based mode feature representation for feature extraction of ship-radiated noise using VMD and slope entropy

To extract more distinguishing features of ships, slope entropy (SloE) is introduced into underwater acoustic signal processing as a new feature to analyze ship-radiated noise signal (S-NS) complexity. SloE can solve the defect that permutation entropy (PE) ignores the amplitude information of time series, and has not been employed to the field of underwater acoustics. On this basis, combined with the variational mode decomposition (VMD) algorithm, a feature extraction method of S-NS based on VMD and SloE is proposed. Firstly, S-NSs are collected by high-precision sensor, and the S-NS are decomposed into a series of the intrinsic mode functions by VMD. Then, the SloE of IMFs are extracted, and the recognition rate is calculated by k-nearest neighbor (KNN) algorithm. Finally, the comparison experiments with permutation entropy (PE), dispersion entropy (DE), reverse dispersion entropy (RDE) and fluctuation dispersion entropy (FDE) are carried out. The experimental results show that under the condition of single feature, SloE has the highest recognition rate; under the condition of multiple features, the feature extraction method based on SloE can attain higher recognition rate under the same number of features, and can realize the effective recognition of S-NSs.

Feature Extraction of Ship-Radiated Noise Based on Hierarchical Dispersion Entropy

The classification and recognition of ship-radiated noise (SRN) is of great significance to the processing of underwater acoustic signals. In order to improve the stability of recognition and more accurately identify SRN, single feature extraction and dual feature extraction based on hierarchical dispersion entropy (HDE) are proposed. For single feature extraction, HDE of the best node among the eight nodes of the third layer decomposition is extracted. For dual feature extraction, HDE of the best two nodes among the 14 nodes of the first-, second-, and third-layer decompositions are required. The results show that the recognition rate of single and dual feature extraction originated from the method based on HDE reaches 85% and 100%, respectively, better than the method of hierarchical reverse dispersion entropy (HRDE) and hierarchical permutation entropy (HPE).

RCMFRDE: Refined Composite Multiscale Fluctuation-Based Reverse Dispersion Entropy for Feature Extraction of Ship-Radiated Noise

Dispersion entropy (DE), as a newly proposed entropy, has achieved remarkable results in its application. In this paper, on the basis of DE, combined with coarse-grained processing, we introduce the fluctuation and distance information of signal and propose the refined composite multiscale fluctuation-based reverse dispersion entropy (RCMFRDE). As an emerging complexity analysis mode, RCMFRDE has been used for the first time for the feature extraction of ship-radiated noise signals to mitigate the loss caused by the misclassification of ships on the ocean. Meanwhile, a classification and recognition method combined with K-nearest neighbor (KNN) came into being, namely, RCMFRDE-KNN. The experimental results indicated that RCMFRDE has the highest recognition rate in the single feature case and up to 100% in the double feature case, far better than multiscale DE (MDE), multiscale fluctuation-based DE (MFDE), multiscale permutation entropy (MPE), and multiscale reverse dispersion entropy (MRDE), and all the experimental results show that the RCMFRDE proposed in this paper improves the separability of the commonly used entropy in the hydroacoustic domain.

Optimized Variational Mode Decomposition and Permutation Entropy with Their Application in Feature Extraction of Ship-Radiated Noise.

The complex and changeable marine environment surrounded by a variety of noise, including sounds of marine animals, industrial noise, traffic noise and the noise formed by molecular movement, not only interferes with the normal life of residents near the port, but also exerts a significant influence on feature extraction of ship-radiated noise (S-RN). In this paper, a novel feature extraction technique for S-RN signals based on optimized variational mode decomposition (OVMD), permutation entropy (PE), and normalized Spearman correlation coefficient (NSCC) is proposed. Firstly, with the mode number determined by reverse weighted permutation entropy (RWPE), OVMD decomposes the target signal into a set of intrinsic mode functions (IMFs). The PE of all the IMFs and SCC between each IMF with the raw signal are then calculated, respectively. Subsequently, feature parameters are extracted through the sum of PE weighted by NSCC for the IMFs. Lastly, the obtained feature vectors are input into the support vector machine multi-class classifier (SVM) to discriminate various types of ships. Experimental results indicate that five kinds of S-RN samples can be accurately identified with a recognition rate of 94% by the proposed scheme, which is higher than other previously published methods. Hence, the proposed method is more advantageous in practical applications.

A novel feature extraction method for ship-radiated noise

To improve the feature extraction of ship-radiated noise in a complex ocean environment, a novel feature extraction method for ship-radiated noise based on complete ensemble empirical mode decomposition with adaptive selective noise (CEEMDASN) and refined composite multiscale fluctuation-based dispersion entropy (RCMFDE) is proposed. CEEMDASN is proposed in this paper which takes into account the high frequency intermittent components when decomposing the signal. In addition, RCMFDE is also proposed in this paper which refines the preprocessing process of the original signal based on composite multi-scale theory. Firstly, the original signal is decomposed into several intrinsic mode functions (IMFs) by CEEMDASN. Energy distribution ratio (EDR) and average energy distribution ratio (AEDR) of all IMF components are calculated. Then, the IMF with the minimum difference between EDR and AEDR (MEDR) is selected as characteristic IMF. The RCMFDE of characteristic IMF is estimated as the feature vectors of ship-radiated noise. Finally, these feature vectors are sent to self-organizing map (SOM) for classifying and identifying. The proposed method is applied to the feature extraction of ship-radiated noise. The result shows its effectiveness and universality.

A comparative study of four multi-scale entropies combined with grey relational degree in classification of ship-radiated noise

In order to improve the classification performance of ship-radiated noise effectively, a comparative study of four multi-scale entropies is carried out by combining with grey relational degree (GRD) and a novel classification method for ship-radiated noise is proposed based on multi-scale reverse dispersion entropy (MRDE) and GRD, named MRDE-GRD. In this paper, MRDE is applied to the complexity feature extraction of ship-radiated noise under different scales, and GRD is used to identify different kinds of ship-radiated noise by calculating the GRD of the ship to be identified and reference ships. Experimental results show that the proposed MRDE-GRD classification method can effectively extract the complexity features of ship-radiated noise, and has better classification performance than the other three classification methods based on multi-scale permutation entropy (MPE), multi-scale weighted permutation entropy (MWPE) and multi-scale dispersion entropy (MDE) combined with GRD, named MPE-GRD, MWPE-GRD, and MDE-GRD. It is proved that the proposed classification method for ship-radiated noise is effective and achieves a recognition rate of 97.75%.

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

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.

Research on feature extraction of ship-radiated noise based on multi-scale reverse dispersion entropy

Aiming at the problem of ship-radiated noise feature extraction under the complex ocean background, a novel nonlinear dynamic analysis method, named multi-scale reverse dispersion entropy (MRDE), is proposed and applied to ship-radiated noise feature extraction. Furthermore, a novel complexity feature extraction method of ship-radiated noise is proposed based on MRDE. MRDE, as an improved algorithm of reverse dispersion entropy (RDE), can describe the complexity of signal from different scales. Firstly, a comparative study of RDE and MRDE is conducted on simulated signal to verify the effectiveness of MRDE; secondly, multi-scale permutation entropy (MPE) and MRDE features of four kinds of ship-radiated noise are extracted, six feature extraction methods of ship-radiated noise based on energy, frequency and complexity are compared, and the recognition rate is calculated by k-Nearest Neighbor (KNN) algorithm; finally, in order to further improve the recognition rate for four kinds of ship-radiated noise, the MRDE features under two scales are extracted and classified. The experimental results show that the proposed feature extraction method of ship-radiated noise based on MRDE has better separability and higher recognition rate; the recognition rate of ship-radiated noise can be further improved by adding one more MRDE feature. Therefore, the proposed feature extraction method can achieve accurate classification of different kinds of ship-radiated noise.

Feature Extraction of Ship-Radiated Noise Based on Enhanced Variational Mode Decomposition, Normalized Correlation Coefficient and Permutation Entropy.

Due to the complexity and variability of underwater acoustic channels, ship-radiated noise (SRN) detected using the passive sonar is prone to be distorted. The entropy-based feature extraction method can improve this situation, to some extent. However, it is impractical to directly extract the entropy feature for the detected SRN signals. In addition, the existing conventional methods have a lack of suitable de-noising processing under the presence of marine environmental noise. To this end, this paper proposes a novel feature extraction method based on enhanced variational mode decomposition (EVMD), normalized correlation coefficient (norCC), permutation entropy (PE), and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, EVMD is utilized to obtain a group of intrinsic mode functions (IMFs) from the SRN signals. The noise-dominant IMFs are then eliminated by a de-noising processing prior to PE calculation. Next, the correlation coefficient between each signal-dominant IMF and the raw signal and PE of each signal-dominant IMF are calculated, respectively. After this, the norCC is used to weigh the corresponding PE and the sum of these weighted PE is considered as the final feature parameter. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to classify the SRN samples. The experimental results demonstrate that the recognition rate of the proposed methodology is up to 100%, which is much higher than the currently existing methods. Hence, the method proposed in this paper is more suitable for the feature extraction of SRN signals.

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

Extracting effective features from ship-radiated noise is an important way to improve the detection and recognition performance of passive sonar. Complexity features of ship-radiated noise have attracted increasing amounts of attention. However, the traditional definition of complexity based on entropy (information stored in the system) is not accurate. To this end, a new statistical complexity measure is proposed in this paper based on spectrum entropy and disequilibrium. Since the spectrum features are unique to the class of the ship, our method can distinguish different ships according to their location in the two-dimensional plane composed of complexity and spectrum entropy (CSEP). To weaken the influence of ocean ambient noise, the intrinsic time-scale decomposition (ITD) is applied to preprocess the data in this study. The effectiveness of the proposed method is validated through a classification experiment of four types of marine vessels. The recognition rate of the ITD-CSEP methodology achieved 94%, which is much higher than that of traditional feature extraction methods. Moreover, the ITD-CSEP is fast and parameter free. Hence, the method can be applied in the real time processing practical applications.

A Feature Extraction Method of Ship-Radiated Noise Based on Fluctuation-Based Dispersion Entropy and Intrinsic Time-Scale Decomposition.

To improve the feature extraction of ship-radiated noise in a complex ocean environment, fluctuation-based dispersion entropy is used to extract the features of ten types of ship-radiated noise. Since fluctuation-based dispersion entropy only analyzes the ship-radiated noise signal in single scale and it cannot distinguish different types of ship-radiated noise effectively, a new method of ship-radiated noise feature extraction is proposed based on fluctuation-based dispersion entropy (FDispEn) and intrinsic time-scale decomposition (ITD). Firstly, ten types of ship-radiated noise signals are decomposed into a series of proper rotation components (PRCs) by ITD, and the FDispEn of each PRC is calculated. Then, the correlation between each PRC and the original signal are calculated, and the FDispEn of each PRC is analyzed to select the Max-relative PRC fluctuation-based dispersion entropy as the feature parameter. Finally, by comparing the Max-relative PRC fluctuation-based dispersion entropy of a certain number of the above ten types of ship-radiated noise signals with FDispEn, it is discovered that the Max-relative PRC fluctuation-based dispersion entropy is at the same level for similar ship-radiated noise, but is distinct for different types of ship-radiated noise. The Max-relative PRC fluctuation-based dispersion entropy as the feature vector is sent into the support vector machine (SVM) classifier to classify and recognize ten types of ship-radiated noise. The experimental results demonstrate that the recognition rate of the proposed method reaches 95.8763%. Consequently, the proposed method can effectively achieve the classification of ship-radiated noise.

A New Feature Extraction Method for Ship-Radiated Noise Based on Improved CEEMDAN, Normalized Mutual Information and Multiscale Improved Permutation Entropy.

Extracting useful features from ship-radiated noise can improve the performance of passive sonar. The entropy feature is an important supplement to existing technologies for ship classification. However, the existing entropy feature extraction methods for ship-radiated noise are less reliable under noisy conditions because they lack noise reduction procedures or are single-scale based. In order to simultaneously solve these problems, a new feature extraction method is proposed based on improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), normalized mutual information (norMI), and multiscale improved permutation entropy (MIPE). Firstly, the ICEEMDAN is utilized to obtain a group of intrinsic mode functions (IMFs) from ship-radiated noise. The noise reduction process is then conducted by identifying and eliminating the noise IMFs. Next, the norMI and MIPE of the signal-dominant IMFs are calculated, respectively; and the norMI is used to weigh the corresponding MIPE result. The multi-scale entropy feature is finally defined as the sum of the weighted MIPE results. Experimental results show that the recognition rate of the proposed method achieves 90.67% and 83%, respectively, under noise free and 5 dB conditions, which is much higher than existing entropy feature extraction algorithms. Hence, the proposed method is more reliable and suitable for feature extraction of ship-radiated noise in practice.

A New Ship-Radiated Noise Feature Extraction Technique Based on Variational Mode Decomposition and Fluctuation-Based Dispersion Entropy.

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 of the original signal is used to get a group of bandwidth-limited intrinsic mode functions (IMFs). Then, the difference between the FDE of each IMF and the original signal is calculated, respectively; the IMF with the smallest difference (SIMF) is selected to calculate the FDE as the feature vector. Finally, the characteristic vectors are sent to the SOM classifier to categorize the original signal. The proposed method is applied to feature extraction of real ship-radiated noise signals. The results show that this method is more precise for ship-radiated noise signals feature extraction.

Feature Extraction of Ship-Radiated Noise Based on Regenerated Phase-Shifted Sinusoid-Assisted EMD, Mutual Information, and Differential Symbolic Entropy.

To improve the recognition accuracy of ship-radiated noise, a feature extraction method based on regenerated phase-shifted sinusoid-assisted empirical mode decomposition (RPSEMD), mutual information (MI), and differential symbolic entropy (DSE) is proposed in this paper. RPSEMD is an improved empirical mode decomposition (EMD) that alleviates the mode mixing problem of EMD. DSE is a new tool to quantify the complexity of nonlinear time series. It not only has high computational efficiency, but also can measure the nonlinear complexity of short time series. Firstly, the ship-radiated noise is decomposed into a series of intrinsic mode functions (IMFs) by RPSEMD, and the DSE of each IMF is calculated. Then, the MI between each IMF and the original signal is calculated; the sum of MIs is taken as the denominator; and each normalized MI (norMI) is obtained. Finally, each norMI is used as the weight coefficient to weight the corresponding DSE, and the weighted DSE (WDSE) is obtained. The WDSEs are sent into the support vector machine (SVM) classifier to classify and recognize three types of ship-radiated noise. The experimental results demonstrate that the recognition rate of the proposed method reaches 98.3333%. Consequently, the proposed WDSE method can effectively achieve the classification of ships.

The Data-Driven Optimization Method and Its Application in Feature Extraction of Ship-Radiated Noise with Sample Entropy

The data-driven method is an important tool in the field of underwater acoustic signal processing. In order to realize the feature extraction of ship-radiated noise (S-RN), we proposed a data-driven optimization method called improved variational mode decomposition (IVMD). IVMD, as an improved method of variational mode decomposition (VMD), solved the problem of choosing decomposition layers for VMD by using a frequency-aided method. Furthermore, a novel method of feature extraction for S-RN, which combines IVMD and sample entropy (SE), is put forward in this paper. In this study, four types of S-RN signals are decomposed into a group of intrinsic mode functions (IMFs) by IVMD. Then, SEs of all IMFs are calculated. SEs are different in the maximum energy IMFs (EIMFs), thus, SE of the EIMF is seen as a novel feature for S-RN. To verify the effectiveness of the proposed method, a comparison has been conducted by comparing features of center frequency and SE of the EIMF by IVMD, empirical mode decomposition (EMD) and ensemble EMD (EEMD). The analysis results show that the feature of S-RN can be obtain efficiently and accurately by using the proposed method.