An Underwater Acoustic Target Recognition Method Based on Spectrograms with Different Resolutions

This paper focuses on the automatic target recognition (ATR) method based on ship-radiated noise and proposes an underwater acoustic target recognition (UATR) method based on ResNet. In the proposed method, a multi-window spectral analysis (MWSA) method is used to solve the difficulty that the traditional time–frequency (T–F) analysis method has in extracting multiple signal characteristics simultaneously. MWSA generates spectrograms with different T–F resolutions through multiple window processing to provide input for the classifier. Because of the insufficient number of ship-radiated noise samples, a conditional deep convolutional generative adversarial network (cDCGAN) model was designed for high-quality data augmentation. Experimental results on real ship-radiated noise show that the proposed UATR method has good classification performance.

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Underwater Acoustic Target Recognition Based on Generative Adversarial Network Data Augmentation

INTER-NOISE and NOISE-CON Congress and Conference Proceedings ◽

10.3397/in-2021-2737 ◽

2021 ◽

Vol 263 (2) ◽

pp. 4558-4564

Author(s):

Minghong Zhang ◽

Xinwei Luo

Keyword(s):

Data Augmentation ◽

Target Recognition ◽

Training Data ◽

Small Samples ◽

Generative Adversarial Network ◽

Data Set ◽

Underwater Acoustic ◽

Adversarial Network ◽

Acoustic Target ◽

The Impact

Underwater acoustic target recognition is an important aspect of underwater acoustic research. In recent years, machine learning has been developed continuously, which is widely and effectively applied in underwater acoustic target recognition. In order to acquire good recognition results and reduce the problem of overfitting, Adequate data sets are essential. However, underwater acoustic samples are relatively rare, which has a certain impact on recognition accuracy. In this paper, in addition of the traditional audio data augmentation method, a new method of data augmentation using generative adversarial network is proposed, which uses generator and discriminator to learn the characteristics of underwater acoustic samples, so as to generate reliable underwater acoustic signals to expand the training data set. The expanded data set is input into the deep neural network, and the transfer learning method is applied to further reduce the impact caused by small samples by fixing part of the pre-trained parameters. The experimental results show that the recognition result of this method is better than the general underwater acoustic recognition method, and the effectiveness of this method is verified.

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Underwater Acoustic Target Recognition Based on Depthwise Separable Convolution Neural Networks

Sensors ◽

10.3390/s21041429 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1429

Author(s):

Gang Hu ◽

Kejun Wang ◽

Liangliang Liu

Keyword(s):

Feature Extraction ◽

Target Recognition ◽

Target Feature ◽

Underwater Acoustic ◽

Radiated Noise ◽

Automatic Feature Extraction ◽

Acoustic Target ◽

And Cluster Analysis ◽

Underwater Target ◽

The One

Facing the complex marine environment, it is extremely challenging to conduct underwater acoustic target feature extraction and recognition using ship-radiated noise. In this paper, firstly, taking the one-dimensional time-domain raw signal of the ship as the input of the model, a new deep neural network model for underwater target recognition is proposed. Depthwise separable convolution and time-dilated convolution are used for passive underwater acoustic target recognition for the first time. The proposed model realizes automatic feature extraction from the raw data of ship radiated noise and temporal attention in the process of underwater target recognition. Secondly, the measured data are used to evaluate the model, and cluster analysis and visualization analysis are performed based on the features extracted from the model. The results show that the features extracted from the model have good characteristics of intra-class aggregation and inter-class separation. Furthermore, the cross-folding model is used to verify that there is no overfitting in the model, which improves the generalization ability of the model. Finally, the model is compared with traditional underwater acoustic target recognition, and its accuracy is significantly improved by 6.8%.

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Time-Frequency Analysis and Target Recognition of HRRP Based on CN-LSGAN, STFT, and CNN

Complexity ◽

10.1155/2021/6664530 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Jianghua Nie ◽

Yongsheng Xiao ◽

Lizhen Huang ◽

Feng Lv

Keyword(s):

Least Squares ◽

Data Augmentation ◽

Short Term Memory ◽

Target Recognition ◽

Signal To Noise Ratio ◽

Recognition Performance ◽

Feature Learning ◽

Generative Adversarial Network ◽

Time Frequency ◽

Adversarial Network

Aiming at the problem of radar target recognition of High-Resolution Range Profile (HRRP) under low signal-to-noise ratio conditions, a recognition method based on the Constrained Naive Least-Squares Generative Adversarial Network (CN-LSGAN), Short-time Fourier Transform (STFT), and Convolutional Neural Network (CNN) is proposed. Combining the Least-Squares Generative Adversarial Network (LSGAN) with the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), the CN-LSGAN is presented and applied to the HRRP denoise. The frequency domain and phase features of HRRP are gained by STFT in order to facilitate feature learning and also match the input data format of the CNN. These experimental results show that the CN-LSGAN has better data augmentation performance and can effectively avoid the model collapse compared to the generative adversarial network (GAN) and LSGAN. Also, the method has better recognition performance than the one-dimensional CNN method and the Long Short-Term Memory (LSTM) network method.

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A Deep Convolutional Neural Network Inspired by Auditory Perception for Underwater Acoustic Target Recognition

Sensors ◽

10.3390/s19051104 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1104 ◽

Cited By ~ 10

Author(s):

Honghui Yang ◽

Junhao Li ◽

Sheng Shen ◽

Guanghui Xu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Auditory Perception ◽

Target Recognition ◽

Neural Mechanism ◽

Experimental Results ◽

Underwater Acoustic ◽

Radiated Noise ◽

Acoustic Target ◽

Convolution Filters

Underwater acoustic target recognition (UATR) using ship-radiated noise faces big challenges due to the complex marine environment. In this paper, inspired by neural mechanisms of auditory perception, a new end-to-end deep neural network named auditory perception inspired Deep Convolutional Neural Network (ADCNN) is proposed for UATR. In the ADCNN model, inspired by the frequency component perception neural mechanism, a bank of multi-scale deep convolution filters are designed to decompose raw time domain signal into signals with different frequency components. Inspired by the plasticity neural mechanism, the parameters of the deep convolution filters are initialized randomly, and the is n learned and optimized for UATR. The n, max-pooling layers and fully connected layers extract features from each decomposed signal. Finally, in fusion layers, features from each decomposed signal are merged and deep feature representations are extracted to classify underwater acoustic targets. The ADCNN model simulates the deep acoustic information processing structure of the auditory system. Experimental results show that the proposed model can decompose, model and classify ship-radiated noise signals efficiently. It achieves a classification accuracy of 81.96%, which is the highest in the contrast experiments. The experimental results show that auditory perception inspired deep learning method has encouraging potential to improve the classification performance of UATR.

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Underwater Acoustic Target Recognition Based on Supervised Feature-Separation Algorithm

Sensors ◽

10.3390/s18124318 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4318 ◽

Cited By ~ 6

Author(s):

Xiaoquan Ke ◽

Fei Yuan ◽

En Cheng

Keyword(s):

Feature Extraction ◽

Target Recognition ◽

Extraction Methods ◽

Fine Tuning ◽

Recognition Method ◽

Separation Algorithm ◽

Underwater Acoustic ◽

Radiated Noise ◽

High Resonance ◽

Acoustic Target

For the purpose of improving the accuracy of underwater acoustic target recognition with only a small number of labeled data, we proposed a novel recognition method, including 4 steps: pre-processing, pre-training, fine-tuning and recognition. The 4 steps can be explained as follows: (1) Pre-processing with Resonance-based Sparsity Signal Decomposition (RSSD): RSSD was firstly utilized to extract high-resonance components from ship-radiated noise. The high-resonance components contain the major information for target recognition. (2) Pre-training with unsupervised feature-extraction: we proposed a one-dimensional convolution autoencoder-decoder model and then we pre-trained the model to extract features from the high-resonance components. (3) Fine-tuning with supervised feature-separation: a supervised feature-separation algorithm was proposed to fine-tune the model and separate the extracted features. (4) Recognition: classifiers were trained to recognize the separated features and complete the recognition mission. The unsupervised pre-training autoencoder-decoder can make good use of a large number of unlabeled data, so that only a small number of labeled data are required in the following supervised fine-tuning and recognition, which is quite effective when it is difficult to collect enough labeled data. The recognition experiments were all conducted on ship-radiated noise data recorded using a sensory hydrophone. By combining the 4 steps above, the proposed recognition method can achieve recognition accuracy of 93.28%, which sufficiently surpasses other traditional state-of-art feature-extraction methods.

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An Underwater Acoustic Target Recognition Method Based on Restricted Boltzmann Machine

Sensors ◽

10.3390/s20185399 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5399

Author(s):

Xinwei Luo ◽

Yulin Feng

Keyword(s):

Feature Extraction ◽

Target Recognition ◽

Restricted Boltzmann Machine ◽

Boltzmann Machine ◽

Recognition Method ◽

Mel Frequency Cepstral Coefficients ◽

Underwater Acoustic ◽

Radiated Noise ◽

Acoustic Target ◽

Cepstral Coefficients

This article focuses on an underwater acoustic target recognition method based on target radiated noise. The difficulty of underwater acoustic target recognition is mainly the extraction of effective classification features and pattern classification. Traditional feature extraction methods based on Low Frequency Analysis Recording (LOFAR), Mel-Frequency Cepstral Coefficients (MFCC), Gammatone-Frequency Cepstral Coefficients (GFCC), etc. essentially compress data according to a certain pre-set model, artificially discarding part of the information in the data, and often losing information helpful for classification. This paper presents a target recognition method based on feature auto-encoding. This method takes the normalized frequency spectrum of the signal as input, uses a restricted Boltzmann machine to perform unsupervised automatic encoding of the data, extracts the deep data structure layer by layer, and classifies the acquired features through the BP neural network. This method was tested using actual ship radiated noise database, and the results show that proposed classification system has better recognition accuracy and adaptability than the hand-crafted feature extraction based method.

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Classification of Space Objects by Using Deep Learning with Micro-Doppler Signature Images

Sensors ◽

10.3390/s21134365 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4365

Author(s):

Kwangyong Jung ◽

Jae-In Lee ◽

Nammoon Kim ◽

Sunjin Oh ◽

Dong-Wook Seo

Keyword(s):

Transfer Learning ◽

Data Augmentation ◽

Doppler Frequency ◽

Extraction Methods ◽

Classification Performance ◽

Reconstruction Method ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Space Objects ◽

Target Characteristics

Radar target classification is an important task in the missile defense system. State-of-the-art studies using micro-doppler frequency have been conducted to classify the space object targets. However, existing studies rely highly on feature extraction methods. Therefore, the generalization performance of the classifier is limited and there is room for improvement. Recently, to improve the classification performance, the popular approaches are to build a convolutional neural network (CNN) architecture with the help of transfer learning and use the generative adversarial network (GAN) to increase the training datasets. However, these methods still have drawbacks. First, they use only one feature to train the network. Therefore, the existing methods cannot guarantee that the classifier learns more robust target characteristics. Second, it is difficult to obtain large amounts of data that accurately mimic real-world target features by performing data augmentation via GAN instead of simulation. To mitigate the above problem, we propose a transfer learning-based parallel network with the spectrogram and the cadence velocity diagram (CVD) as the inputs. In addition, we obtain an EM simulation-based dataset. The radar-received signal is simulated according to a variety of dynamics using the concept of shooting and bouncing rays with relative aspect angles rather than the scattering center reconstruction method. Our proposed model is evaluated on our generated dataset. The proposed method achieved about 0.01 to 0.39% higher accuracy than the pre-trained networks with a single input feature.

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Combination of gated recurrent unit and Network in Network for underwater acoustic target recognition

INTER-NOISE and NOISE-CON Congress and Conference Proceedings ◽

10.3397/in-2021-1490 ◽

2021 ◽

Vol 263 (6) ◽

pp. 486-492

Author(s):

Shuang Yang ◽

Xiangyang Zeng

Keyword(s):

Information Acquisition ◽

Target Recognition ◽

Recognition Rate ◽

Temporal Correlation ◽

Feedback Mechanism ◽

Underwater Acoustic ◽

Acoustic Information ◽

Proposed Model ◽

Acoustic Target ◽

Gated Recurrent Unit

Underwater acoustic target recognition is an important part of underwater acoustic signal processing and an important technical support for underwater acoustic information acquisition and underwater acoustic information confrontation. Taking into account that the gated recurrent unit (GRU) has an internal feedback mechanism that can reflect the temporal correlation of underwater acoustic target features, a model with gated recurrent unit and Network in Network (NIN) is proposed to recognize underwater acoustic targets in this paper. The proposed model introduces NIN to compress the hidden states of GRU while retaining the original timing characteristics of underwater acoustic target features. The higher recognition rate and faster calculation speed of the proposed model are demonstrated with experiments for raw underwater acoustic signals comparing with the multi-layer stacked GRU model.

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