scholarly journals Study on Active Tracking of Underwater Acoustic Target Based on Deep Convolution Neural Network

2021 ◽  
Vol 11 (16) ◽  
pp. 7530
Author(s):  
Maofa Wang ◽  
Baochun Qiu ◽  
Zeifei Zhu ◽  
Huanhuan Xue ◽  
Chuanping Zhou
Keyword(s):  
Neural Network ◽  
Research Direction ◽  
Distance Information ◽  
Underwater Acoustic ◽  
Time Frequency ◽  
Acoustic Target ◽  
Deep Convolution Neural Network ◽  
Underwater Acoustic Signal ◽  
Tracking Technology ◽  
Important Research Direction

The active tracking technology of underwater acoustic targets is an important research direction in the field of underwater acoustic signal processing and sonar, and it has always been issued that draws researchers’ attention. The commonly used Kalman filter active tracking (KFAT) method is an effective tracking method, however, it is difficult to detect weak SNR signals, and it is easy to lose the target after the azimuth of different targets overlaps. This paper proposes a KFAT based on deep convolutional neural network (DCNN) method, which can effectively solve the problem of target loss. First, we use Kalman filtering to predict the azimuth and distance of the target, and then use the trained model to identify the azimuth-weighted time-frequency image to obtain the azimuth and label of the target and obtain the target distance by the time the target appears in the time-frequency image. Finally, we associate the data according to the target category, and update the target azimuth and distance information for this cycle. In this paper, two methods, KFAT and DCNN-KFAT, are simulated and tested, and the results are obtained for two cases of tracking weak signal-to-noise signals and tracking different targets with overlapping azimuths. The simulation results show that the DCNN-KFAT method can solve the problem that the KFAT method is difficult to track the target under the weak SNR and the problem that the target is easily lost when two different targets overlap in azimuth. It reduces the deviation range of the active tracking to within 200 m, which is 500~700 m less than the KFAT method.

scholarly journals Underwater Acoustic Target Recognition: A Combination of Multi-Dimensional Fusion Features and Modified Deep Neural Network

2019 ◽  
Vol 11 (16) ◽  
pp. 1888 ◽  
Author(s):  
Xingmei Wang ◽  
Anhua Liu ◽  
Yu Zhang ◽  
Fuzhao Xue
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Target Recognition ◽  
Acoustic Signals ◽  
Gaussian Mixture ◽  
Time Dimension ◽  
Underwater Acoustic ◽  
Mode Decomposition ◽  
Acoustic Target ◽  
Fusion Features

A method with a combination of multi-dimensional fusion features and a modified deep neural network (MFF-MDNN) is proposed to recognize underwater acoustic targets in this paper. Specifically, due to the complex and changeable underwater environment, it is difficult to describe underwater acoustic signals with a single feature. The Gammatone frequency cepstral coefficient (GFCC) and modified empirical mode decomposition (MEMD) are developed to extract multi-dimensional features in this paper. Moreover, to ensure the same time dimension, a dimension reduction method is proposed to obtain multi-dimensional fusion features in the original underwater acoustic signals. Then, to reduce redundant features and further improve recognition accuracy, the Gaussian mixture model (GMM) is used to modify the structure of a deep neural network (DNN). Finally, the proposed underwater acoustic target recognition method can obtain an accuracy of 94.3% under a maximum of 800 iterations when the dataset has underwater background noise with weak targets. Compared with other methods, the recognition results demonstrate that the proposed method has higher accuracy and strong adaptability.

scholarly journals A New Hybrid Model Based on Fruit Fly Optimization Algorithm and Wavelet Neural Network and Its Application to Underwater Acoustic Signal Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Hong Yang ◽  
Siliang Wang ◽  
Guohui Li ◽  
Tongtong Mao
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Acoustic Signal ◽  
Fruit Fly ◽  
Wavelet Neural Network ◽  
Fruit Fly Optimization Algorithm ◽  
Underwater Acoustic ◽  
Fruit Fly Optimization ◽  
Neural Network Prediction ◽  
Underwater Acoustic Signal

The local predictability of underwater acoustic signal plays an important role in underwater acoustic signal processing, and it is the basis of nonstationary signal detection. Wavelet neural network model, with the advantages of both wavelet analysis and artificial neural network, makes full use of the time-frequency localization characteristics of wavelet analysis and the self-learning ability of artificial neural network; however, this model is prone to fall into local minima or creates convergence. To overcome these disadvantages, a new hybrid model based on fruit fly optimization algorithm (FOA) and wavelet neural network (WNN) is proposed in this paper. The FOA-WNN prediction model is constructed by optimizing the weights and thresholds of wavelet neural network, and the model is applied to underwater acoustic signal prediction. The experimental results show that the FOA-WNN prediction model has higher prediction accuracy and smaller prediction error, compared with wavelet neural network prediction model and BP neural network prediction model.

scholarly journals Image recognition algorithms based on deep learning

2021 ◽  
Vol 2137 (1) ◽  
pp. 012056
Author(s):  
Hongli Ma ◽  
Fang Xie ◽  
Tao Chen ◽  
Lei Liang ◽  
Jie Lu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Development Process ◽  
Research Direction ◽  
Learning Technology ◽  
Convolutional Network ◽  
Important Research Direction

Abstract Convolutional neural network is a very important research direction in deep learning technology. According to the current development of convolutional network, in this paper, convolutional neural networks are induced. Firstly, this paper induces the development process of convolutional neural network; then it introduces the structure of convolutional neural network and some typical convolutional neural networks. Finally, several examples of the application of deep learning is introduced.

Underwater acoustic target classification based on u-shaped network

2021 ◽  
Vol 263 (5) ◽  
pp. 1338-1345
Author(s):  
Xue Lingzhi ◽  
Zeng Xiangyang
Keyword(s):  
Network Architecture ◽  
Feature Maps ◽  
Generative Adversarial Network ◽  
Underwater Acoustic ◽  
Adversarial Network ◽  
Label Information ◽  
Acoustic Target ◽  
Feature Information ◽  
Underwater Acoustic Signal ◽  
Better Than

Target recognition is a key task and a difficult technique in underwater acoustic signal processing. One of the most challenging problem is that the label information of the underwater acoustic samples is scarce or missing. To solve the problem, this paper presents a local skip connection u-shaped architecture network(U-Net)based on the convolutional neural network(CNN).To this end, the network architecture is designed cleverly to generate a contracting path and an expansive path to achieve the extraction of different scale features. More importantly, a local skip connection mechanism is proposed to optimize classification rates by reusing former feature maps in contracting path. The experimental results of the measured dataset demonstrate the recognition accuracy of the model is better than that of deep belief network(DBN) and generative adversarial network(GAN) networks.Further research on three kinds of network by visualization method shows that the proposed network can learn more effective feature information with limited samples.

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