Research on Feature Extraction and Recognition Method of Underwater Acoustic Target Based on Deep Convolutional Network

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.

Download Full-text

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.

Download Full-text

A Novel Feature Extraction Method for Underwater Acoustic Target Based on Parameter Optimized VMD and 1(1/2)-D Spectrum

Proceedings of the 2020 4th International Conference on Digital Signal Processing ◽

10.1145/3408127.3408149 ◽

2020 ◽

Author(s):

Dan Xu ◽

Huiwen Zheng ◽

Qiao Hu

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Underwater Acoustic ◽

Feature Extraction Method ◽

Acoustic Target

Download Full-text

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%.

Download Full-text

A Fast Instantaneous Frequency Estimation for Underwater Acoustic Target Feature Extraction

Journal of Physics Conference Series ◽

10.1088/1742-6596/2031/1/012018 ◽

2021 ◽

Vol 2031 (1) ◽

pp. 012018

Author(s):

Yanxin Ma ◽

Yifan Zhang ◽

Jiahua Zhu ◽

Ke Xu ◽

Yujin Cai

Keyword(s):

Feature Extraction ◽

Instantaneous Frequency ◽

Frequency Estimation ◽

Target Feature ◽

Instantaneous Frequency Estimation ◽

Underwater Acoustic ◽

Acoustic Target

Download Full-text

Corrigendum: A Fast Instantaneous Frequency Estimation for Underwater Acoustic Target Feature Extraction (2021 J. Phys.: Conf. Ser. Vol 2031 012018)

Journal of Physics Conference Series ◽

10.1088/1742-6596/2031/1/012069 ◽

2021 ◽

Vol 2031 (1) ◽

pp. 012069

Author(s):

Yanxin Ma ◽

Yifan Zhang ◽

Jiahua Zhu ◽

Ke Xu ◽

Yujin Cai

Keyword(s):

Feature Extraction ◽

Instantaneous Frequency ◽

Frequency Estimation ◽

Target Feature ◽

Instantaneous Frequency Estimation ◽

Underwater Acoustic ◽

Acoustic Target

Abstract Please see Article PDF for corrigendum text.

Download Full-text

Intelligent Defect Identification Based on PECT Signals and an Optimized Two-Dimensional Deep Convolutional Network

Complexity ◽

10.1155/2020/9518945 ◽

2020 ◽

Vol 2020 ◽

pp. 1-18

Author(s):

Baoling Liu ◽

Jun He ◽

Xiaocui Yuan ◽

Huiling Hu ◽

Xuan Zeng ◽

...

Keyword(s):

Feature Extraction ◽

Eddy Current ◽

Computational Cost ◽

Eddy Current Testing ◽

Two Dimensional ◽

Convolutional Network ◽

Defect Identification ◽

Current Testing ◽

Pulsed Eddy Current ◽

Deep Convolutional Network

Accurate and rapid defect identification based on pulsed eddy current testing (PECT) plays an important role in the structural integrity and health monitoring (SIHM) of in-service equipment in the renewable energy system. However, in conventional data-driven defect identification methods, the signal feature extraction is time consuming and requires expert experience. To avoid the difficulty of manual feature extraction and overcome the shortcomings of the classic deep convolutional network (DCNN), such as large memory and high computational cost, an intelligent defect recognition pipeline based on the general Warblet transform (GWT) method and optimized two-dimensional (2-D) DCNN is proposed. The GWT method is used to convert the one-dimensional (1-D) PECT signal to a 2D grayscale image used as the input of 2D DCNN. A compound method is proposed to optimize the baseline VGG16, a well-known DCNN, from four aspects including reducing the input size, adding batch normalization layer (BN) after every convolutional layer(Conv) and fully connection layer (FC), simplifying the FCs, and removing unimportant filters in Convs so as to reduce memory and computational costs while improving accuracy. Through a pulsed eddy current testing (PECT) experiment considering interference factors including liftoff and noise, the following conclusion can be obtained. The time-frequency representation (TFR) obtained by the GWT method not only has excellent ability in terms of the transient component analysis but also is less affected by the reduction of image size; the proposed optimized DCNN can accurately identify defect types without manual feature extraction. And compared to the baseline VGG16, the accuracy obtained by the optimized DCNN is improved by 7%, to about 99.58%, and the memory and computational cost are reduced by 98%. Moreover, compared with other well-known DCNNs, such as GoogLeNet, Inception V3, ResNet50, and AlexNet, the optimized network has significant advantages in terms of accuracy and computational cost, too.

Download Full-text