Ship-radiated Noise Research Articles

Detection method of ship-radiated noise based on fractional-order dual coupling oscillator

Detection method of ship-radiated noise based on fractional-order dual coupling oscillator

Variable-Step Multiscale Katz Fractal Dimension: A New Nonlinear Dynamic Metric for Ship-Radiated Noise Analysis

The Katz fractal dimension (KFD) is an effective nonlinear dynamic metric that characterizes the complexity of time series by calculating the distance between two consecutive points and has seen widespread applications across numerous fields. However, KFD is limited to depicting the complexity of information from a single scale and ignores the information buried under different scales. To tackle this limitation, we proposed the variable-step multiscale KFD (VSMKFD) by introducing a variable-step multiscale process in KFD. The proposed VSMKFD overcomes the disadvantage that the traditional coarse-grained process will shorten the length of the time series by varying the step size to obtain more sub-series, thus fully reflecting the complexity of information. Three simulated experimental results show that the VSMKFD is the most sensitive to the frequency changes of a chirp signal and has the best classification effect on noise signals and chaotic signals. Moreover, the VSMKFD outperforms five other commonly used nonlinear dynamic metrics for ship-radiated noise classification from two different databases: the National Park Service and DeepShip.

Using Feature Extraction to Perform Equipment Health Monitoring on Ship-Radiated Noise

The current state of the art in hydroacoustics research employs a variety of feature extraction techniques with the goal of accurately classifying a ship based on its radiated noise. These techniques are capable of accuracy in excess of 95%. A question arises as to whether similar techniques could be applied to a known vessel to identify and monitor individual systems from the ship’s noise. In this paper, the fast orthogonal search algorithm is used as a basis for a feature extraction and classification algorithm. This algorithm is applied to real recordings of ship-radiated noise and is shown to be capable of identifying the running status of a subset of the ship’s systems, providing a proof of concept for the detection and monitoring of a ship’s systems based solely on the ships hydroacoustic noise.

An effective hybrid deep neural network for underwater acoustic target recognition

Underwater acoustic target recognition is an important support technology for underwater information detection and signal processing. Due to the high complexity of the marine environment, it is not easy to obtain clear and reliable features from ship-radiated noise, which makes underwater acoustic target recognition difficult. We propose a new hybrid network of transformer networks and convolutional neural networks to alleviate this problem. It consists of three convolution modules and a transformer module. The convolution modules are used to learn the local features of the time-frequency map, and the features are enhanced using the residual structure and channel attention. The transformer module is used to extract the global features of the time-frequency map. The proposed network can obtain global and local features of the ship-radiated noise from the time-frequency map of the raw data, which can improve the accuracy of recognition. Experiments were conducted on DeepShip, a public benchmark dataset of ship-radiated noise, and compared with the current state-of-the-art methods. The results show that the proposed hybrid network achieves the highest recognition accuracy on the DeepShip dataset.

A ship-radiated noise classification method based on domain knowledge embedding and attention mechanism

Ship classification based on machine learning (ML) has proven to be a significant underwater acoustic research direction. One of the critical challenges rests with how to embed domain signal knowledge into ML models to obtain suitable features that highly correlate with the classification and create better predictors. In this paper, a novel ML-based ship classification model, Hierarchical Underwater Acoustic Transformer (HUAT), is proposed to improve the classification performance. Firstly, the Detection of Envelope Modulation on Noise (DEMON) spectra of ship-radiated noise signals are estimated by cyclostationary analysis. The motivation for using a DEMON-based preprocessing scheme is that valuable propeller information can be revealed by exploiting the second-order cyclostationarity of ship-radiated noise signals. Secondly, the useful features of DEMON spectra are enhanced using a multi-head self-attention module, and the potential features of the Mel spectrograms are extracted employing a Convolutional Neural Network (CNN) module. The two kinds of features are fused to provide ship classification patterns. The challenge of feature learning in the deep classification model is reduced by leveraging domain-related classification knowledge. Finally, the Swin Transformer, based on shifted window self-attention mechanism, is used to learn high-level feature representations and conduct ship classification. Experimental results show that the HUAT model achieves excellent classification performance on ship-radiated noise datasets, ShipsEar and DeepShip. And its classification efficiency is better than the model based on traditional Transformer architecture. In addition, the proposed method provides technical support for the underwater intelligent system capable of automatically sensing sailing vessels and recognizing vessel types.

Dual feature extraction system for ship-radiated noise and its application extension

The complexity of maritime noise and the time-variability of hydroacoustic channels make it more and more difficult to extract feature from ship-radiated noise effectively. To accurately recognize ship-radiated noise, a dual feature extraction and recognition system based on pre-processing-dual feature extraction-recognition is proposed. Pre-processing: The primary decomposition and secondary decomposition are performed for the first time using variational mode decomposition optimized by giant trevally optimizer, in which the threshold is adaptively determined. Dual feature extraction: Grey relational analysis is calculated for the intrinsic mode function component after primary decomposition and the high-intrinsic mode function component after secondary decomposition, and two eigenvectors are selected separately. Then entropy of the combination of weighted fluctuation-based dispersion entropy and Lempel-Ziv complexity is proposed to calculate the eigenvalues for classification. Recognition: The feature values are recognized using random forest recognizer optimized by tuna swarm optimization to obtain the final recognition rate. The experimental results show that the proposed entropy has an overwhelming advantage over the others, such as permutation entropy, and dispersion entropy. It is proved that the proposed system for ship-radiated noise has good classification performance and the recognition rate is up to 97.5%. In addition, it is successfully applied to simulated chaotic signals as well as measured aquatic life signals.

A novel feature extraction method for ship-radiated noise based on hierarchical refined composite multi-scale dispersion entropy-based Lempel-Ziv complexity

Accurate recognition of ship-radiated noise (SRN) is particularly important for coastal defense, where feature extraction is the key technology. As a recently proposed nonlinear dynamics index, dispersion entropy-based Lempel-Ziv complexity (DELZC) can effectively quantify the information contained in SRN. However, DELZC characterizes inadequately the complexity feature of SRN with the ignorance of the information under different scales and information hidden in high frequency. To correct these drawbacks, the refined composite coarse-graining operation is integrated into DELZC, and the refined composite multi-scale DELZC (RCMDELZC) is put forward and adequately reflects the scale information, subsequently the technology of hierarchical decomposition is introduced and proposed the hierarchical RCMDELZC (HRCMDELZC), which takes into account the abundant feature information of the signal contained in the time scale and different frequency bands. Then, on the basis of HRCMDELZC, a feature extraction method for SRN by combining with maximum relevance minimum redundancy (mRMR) is proposed. The simulation results show that HRCMDELZC can effectively classify different noise and chaotic signals; the realistic experimental results deliver that the HRCMDELZC features of SRNs have more excellent separability compared with the other seven categories of complexity features, and the proposed feature extraction method has the highest classification accuracy for various SRNs.

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.

Improved pitch shifting data augmentation for ship-radiated noise classification

The limited amount of ship-radiated noise data causes machine learning models to be prone to overfitting in training, and data augmentation methods could improve model generalization performance. The frequency stability of harmonic line spectra in ship-radiated noise leads to a lack of sample diversity generated by the pitch shifting method, which is overcome by the proposed improved method. Nine classification algorithms combining three time-frequency features and three classifiers are implemented and evaluated on the DeepShip and the ShipsEar datasets. The average accuracy increased by 1.67% on DeepShip and 2.25% on ShipsEar, using improved pitch shifting and time stretching augmentation methods. The constant-Q transformation-convolutional neural networks (CQT-CNN) performs best among these nine algorithms. Its accuracy improved from 68.33% to 74.08% on the DeepShip, and the F1 score of it improved from 57.92% to 61.45% on the ShipsEar. Data augmentation improves classification performance for each class of ships in different ways, suggesting that augmentation specific to the class and state of the ship would improve classification performance further.

SVMD coupled with dual-threshold criteria of correlation coefficient: A self-adaptive denoising method for ship-radiated noise signal

Due to the complexity of the marine environment, ship-radiated noise signal (SRNS) often contains harsh marine background noise. Hence extracting the features of SRNS becomes very difficult. Aim to eliminate the interference of background noise, this paper proposes a self-adaptive denoising method for SRNS, called SVMD-Dual-CC-WST, which not only uses the successive variational mode decomposition (SVMD) as a theoretical basis but also combines with the dual-threshold screening of the correlation coefficient (Dual-CC) criterion and wavelet soft threshold (WST). Firstly, different original signals are decomposed into several intrinsic mode functions (IMFs) by SVMD. Then, the CC of the IMFs is calculated, and the IMFs are adaptively divided into three categories using the Dual-CC criterion, including signal IMFs, noisy IMFs, and noise IMFs. Finally, the noise IMFs are discarded, and the noisy IMFs are denoised with WST and then reconstructed with the signal IMFs to obtain the denoising signals. Simulated signals and SRNS experiments prove the effectiveness of the proposed method.

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.

SO-slope entropy coupled with SVMD: A novel adaptive feature extraction method for ship-radiated noise

Slope entropy (SloEn) has been applied as a powerful nonlinear dynamic tool for signal complexity measurement and is widely used for ship-radiated noise signal (S-RNS) feature extraction. However, the thresholds of SloEn affect the entropy value, which influences the effect of feature extraction. Aimed at addressing the problem, this paper uses the snake optimizer (SO) to optimize SloEn and proposes a new entropy indicator, named SO SloEn (SO-SloEn), and then a novel adaptive S-RNS feature extraction method is put forward in combination with the successive variational mode decomposition (SVMD) and SO-SloEn, which solves the parameter selection problem of variational mode decomposition (VMD). First, SVMD is employed to decompose the S-RNS into several intrinsic mode functions (IMFs); after that, the SO-SloEn of IMFs is calculated to obtain the feature matrix dataset; finally, optimal IMF combinations are obtained through feature selection. The effectiveness of the proposed method is verified by two S-RNS cases, and the results indicate that the recognition rate of the proposed method is always the highest compared with other decomposition algorithms and other entropy indicators under the condition of extracting different numbers of IMFs; moreover, the highest recognition rate can reach more than 92% in both cases.

Bayesian geoacoustic inversion in shallow water with vertical coherence of ship-radiated noise

Spatial structure of underwater acoustic field depends strongly on the geoacoustic properties of the seabed. The geoacoustic parameters can be estimated using the spatial coherence dominated by a single broadband source. In this paper, geoacoustic inversion of a shallow water environment is carried out using the vertical coherence of ship-radiated noise at a close range. Data were measured in an experiment in the South China Sea, and the vertical coherence is extracted from a single hydrophone pair in the vertical line array. The closest point of the passing research ship approach is determined first, and then the broadband coherence curves are extracted as the input for the inversion algorithm. Due to the lack of an appropriate parametrization, the trans-dimensional inversion method combined with the auto-regressive error model is adopted in this paper. The inversion results show that the sound speed is the most sensitive, followed by the density, and then the attenuation coefficient is the least sensitive, which is consistent with previous works. The data error hypothesis is verified via a posterior examinations.

Dual-branch time–frequency domain anti-interference method for ship radiated noise signal

Sensing the target ship using the radiated noise of the ship is the main means of sensing the target ship. In the adversarial and non-cooperative scenarios, explosion interference is a typical interference source used to mask the target ship and confuse the underwater acoustic sensing system to effectively perceive the target. In this paper, we try to use the idea of signal enhancement to deal with the anti-explosion interference. we try to enhance the ship radiated noise signal with extremely low signal-to-noise ratio, so as to achieve anti-explosion signal interference. We propose a deep learning approach with encoder–decoder as the basic framework. We design a decoder with two branches, one for estimating the he mask of the noise signal amplitude spectrogram and the other for estimating the complex spectrogram. We add the attention mechanism to the model to select features relevant to each branch task. Finally, we use the real experimental data to test, the experimental results show that our method can enhance the ship radiation noise signal to 2 dB under the condition of −20 dB to −25 dB very low signal-to-noise ratio. The experimental results fully prove that our method can achieve anti-explosion interference of ship radiated noise.

A Novel Underwater Acoustic Target Identification Method Based on Spectral Characteristic Extraction via Modified Adaptive Chirp Mode Decomposition

As is well-known, ship-radiated noise (SN) signals, which contain a large number of ship operating characteristics and condition information, are widely used in ship recognition and classification. However, it is still a great challenge to extract weak operating characteristics from SN signals because of heavy noise and non-stationarity. Therefore, a new mono-component extraction method is proposed in this paper for taxonomic purposes. First, the non-local means algorithm (NLmeans) is proposed to denoise SN signals without destroying its time-frequency structure. Second, adaptive chirp mode decomposition (ACMD) is modified and applied on denoised signals to adaptively extract mono-component modes. Finally, sub-signals are selected based on spectral kurtosis (SK) and then analyzed for ship recognition and classification. A simulation experiment and two application cases are used to verify the effectiveness of the proposed method and the results show its outstanding performance.

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.

Feature extraction method of ship-radiated noise based on dispersion entropy: A review

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.

A Lightweight Network Model Based on an Attention Mechanism for Ship-Radiated Noise Classification

Recently, deep learning has been widely used in ship-radiated noise classification. To improve classification efficiency, avoiding high computational costs is an important research direction in ship-radiated noise classification. We propose a lightweight squeeze and excitation residual network 10 (LW-SEResNet10). In ablation experiments of LW-SEResNet10, the use of ResNet10 instead of ResNet18 reduced 56.1% of parameters, while the accuracy is equivalent to ResNet18. The improved accuracy indicates that the ReLU6 enhanced the model stability, and an attention mechanism captured the channel dependence. The ReLU6 activation function does not introduce additional parameters, and the number of parameters introduced by the attention mechanism accounts for 0.2‰ of the model parameters. The 3D dynamic MFCC feature performs better than MFCC, Mel-spectrogram, 3D dynamic Mel-spectrogram, and CQT. Moreover, the LW-SEResNet10 model is also compared with ResNet and two classic lightweight models. The experimental results show that the proposed model achieves higher classification accuracy and is lightweight in terms of not only the model parameters, but also the time consumption. LW-SEResNet10 also outperforms the state-of-the-art model CRNN-9 by 3.1% and ResNet by 3.4% and has the same accuracy as AudioSet pretrained STM, which achieves the trade-off between accuracy and model efficiency.

Data augmentation and deep neural network classification based on ship radiated noise

IntroductionVarious types of ships sail at sea, and identifying maritime ship types through shipradiated noise is one of the tasks of ocean observation. The ocean environment is complex and changeable, such rapid environmental changes underline the difficulties of obtaining a huge amount of samples. Meanwhile, the length of each sample has a decisive influence on the classification results, but there is no universal sampling length selection standard.MethodsThis study proposes an effective framework for ship-radiated noise classification. The framework includes: i) A comprehensive judgment method based on multiple features for sample length selecting. ii) One-dimensional deep convolution generative adversarial network (1-DDCGAN) model to augment the training datasets for small sample problem. iii) One-dimensional convolution neural network (CNN) trained by generated data and real data for ship-radiated noise classification. On this basis, a onedimensional residual network (ResNet) is designed to improve classification accuracy.ResultsExperiments are performed to verify the proposed framework using public datasets. After data augmentation, statistical parameters are used to measure the similarity between the original samples and the generated samples. Then, the generated samples are integrated into the training set. The convergence speed of the network is clearly accelerated, and the classification accuracy is significantly improved in the one-dimensional CNN and ResNet.DiscussionIn this study, we propose an effective framework for the lack of scientific sample length selection and lack of sample number in the classification of ship-radiated noise, but there aret still some problems: high complexity, structural redundancy, poor adaptability, and so on. They are also long-standing problems in this field that needs to be solved urgently.

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.