scholarly journals An Underwater Acoustic Target Recognition Method Based on Restricted Boltzmann Machine

Sensors ◽  
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.

scholarly journals Underwater Acoustic Target Recognition Based on Supervised Feature-Separation Algorithm

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4318 ◽  
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.

scholarly journals Underwater Acoustic Target Recognition Based on Depthwise Separable Convolution Neural Networks

Sensors ◽  
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%.

Multi-scale spectral feature extraction for underwater acoustic target recognition

Measurement ◽  
2020 ◽  
Vol 166 ◽  
pp. 108227
Author(s):  
Junjun Jiang ◽  
Tuo Shi ◽  
Min Huang ◽  
Zhongzhe Xiao
Keyword(s):  
Feature Extraction ◽  
Target Recognition ◽  
Spectral Feature ◽  
Underwater Acoustic ◽  
Multi Scale ◽  
Acoustic Target

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

2021 ◽  
Vol 9 (11) ◽  
pp. 1246
Author(s):  
Xinwei Luo ◽  
Minghong Zhang ◽  
Ting Liu ◽  
Ming Huang ◽  
Xiaogang Xu
Keyword(s):  
Data Augmentation ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Classification Performance ◽  
Quality Data ◽  
Generative Adversarial Network ◽  
Underwater Acoustic ◽  
Time Frequency ◽  
Radiated Noise ◽  
Acoustic Target

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.

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

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1104 ◽  
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.

scholarly journals Deep Learning Methods for Underwater Target Feature Extraction and Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Gang Hu ◽  
Kejun Wang ◽  
Yuan Peng ◽  
Mengran Qiu ◽  
Jianfei Shi ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extreme Learning Machine ◽  
Acoustic Signal ◽  
Recognition Rate ◽  
Mel Frequency Cepstral Coefficients ◽  
Underwater Acoustic ◽  
Cepstral Coefficients ◽  
Underwater Target ◽  
Learning Machine ◽  
Underwater Acoustic Signal

The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved.

scholarly journals Underwater Acoustic Target Recognition with a Residual Network and the Optimized Feature Extraction Method

2021 ◽  
Vol 11 (4) ◽  
pp. 1442
Author(s):  
Feng Hong ◽  
Chengwei Liu ◽  
Lijuan Guo ◽  
Feng Chen ◽  
Haihong Feng
Keyword(s):  
Feature Extraction ◽  
Target Recognition ◽  
Three Dimensional ◽  
Recognition Task ◽  
Residual Network ◽  
Underwater Acoustic ◽  
Feature Extraction Method ◽  
Time Space ◽  
Acoustic Target ◽  
The Impact

Underwater Acoustic Target Recognition (UATR) remains one of the most challenging tasks in underwater signal processing due to the lack of labeled data acquisition, the impact of the time-space varying intrinsic characteristics, and the interference from other noise sources. Although some deep learning methods have been proven to achieve state-of-the-art accuracy, the accuracy of the recognition task can be improved by designing a Residual Network and optimizing feature extraction. To give a more comprehensive representation of the underwater acoustic signal, we first propose the three-dimensional fusion features along with the data augment strategy of SpecAugment. Afterward, an 18-layer Residual Network (ResNet18), which contains the center loss function with the embedding layer, is designed to train the aggregated features with an adaptable learning rate. The recognition experiments are conducted on the ship-radiated noise dataset from a real environment, and the accuracy results of 94.3% indicate that the proposed method is appropriate for underwater acoustic recognition problems and sufficiently surpasses other classification methods.

Sign in / Sign up

Export Citation Format

Share Document