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Integrated neural networks based on feature fusion for underwater target recognition

机译：基于水下目标识别特征融合的集成神经网络

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摘要

Currently, traditional feature extraction algorithms have poor data expression and noise robustness. Moreover, traditional recognition methods are gradually falling behind the demand for increasing data, and struggle to extract deep features in targets. By considering the preceding issues, an integrated neural network has been created in this paper for underwater acoustic target recognition via feature fusion learning. Firstly, the short time Fourier transform (STFT) amplitude spectrum, STFT phase spectrum, and bispectrum feature of underwater acoustic signals are extracted and form the input for the network. They not only contain rich information about the target, but also have strong noise robustness. Secondly, an integrated neural network has been designed, which is trained with different features and contains three neural networks. Finally, in the softmax layer of the network, the shuffled frog leaping algorithm (SFLA) is utilized to train the weight coefficients of different networks. Experimental results of the measured data show that the integrated neural network method based on feature fusion has a higher recognition accuracy and stronger noise robustness. (C) 2021 Elsevier Ltd. All rights reserved.
机译：目前，传统的特征提取算法具有差的数据表达和噪音鲁棒性。此外，传统的识别方法逐渐落后于增加数据的需求，并努力提取目标中的深度特征。通过考虑前面的问题，通过特征融合学习，本文在本文中创建了一个集成的神经网络，以便通过特征融合学习。首先，提取短时间傅里叶变换（STFT）幅度频谱，STFT相波和BISPectrum特征，并形成网络的输入。它们不仅包含有关目标的丰富信息，而且还具有强大的噪音鲁棒性。其次，设计了一个集成的神经网络，其具有不同的特征，并包含三个神经网络。最后，在网络的Softmax层中，随机交叉跨越算法（SFLA）用于训练不同网络的权重系数。测量数据的实验结果表明，基于特征融合的集成神经网络方法具有更高的识别精度和更强的噪声鲁棒性。 （c）2021 elestvier有限公司保留所有权利。

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  • 来源
    《Applied Acoustics》 |2021年第11期|108261.1-108261.9|共9页
  • 作者

    Zhang Qi; Da Lianglong; Zhang Yanhou; Hu Yaohui;

  • 作者单位

    Naval Submarine Acad Qingdao 266199 Peoples R China|Pilot Natl Lab Marine Sci & Technol Qingdao 266237 Peoples R China;

    Naval Submarine Acad Qingdao 266199 Peoples R China|Pilot Natl Lab Marine Sci & Technol Qingdao 266237 Peoples R China;

    Naval Submarine Acad Qingdao 266199 Peoples R China|Pilot Natl Lab Marine Sci & Technol Qingdao 266237 Peoples R China;

    Naval Submarine Acad Qingdao 266199 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Feature fusion; Neural network; Short time Fourier transform; Bispectrum; Shuffled frog leaping algorithm;

    机译：特征融合;神经网络;短时间傅里叶变换;BISPECTRUM;随机交叉跳跃算法;

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