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Volume 28 Issue 2
 
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Article Navigation >  Journal of Unmanned Undersea Systems  > 2020  >  28(2): 162-167
LI Qi-fei, WU Fang, HAN Lei-lei, FAN Zhao-peng, LI Pei-zong. Detection Method of Magnetic Anomaly Signals Based on AlexNet Transfer Learning[J]. Journal of Unmanned Undersea Systems, 2020, 28(2): 162-167. doi: 10.11993/j.issn.2096-3920.2020.02.007
Citation: LI Qi-fei, WU Fang, HAN Lei-lei, FAN Zhao-peng, LI Pei-zong. Detection Method of Magnetic Anomaly Signals Based on AlexNet Transfer Learning[J]. Journal of Unmanned Undersea Systems, 2020, 28(2): 162-167. doi: 10.11993/j.issn.2096-3920.2020.02.007
shu

Detection Method of Magnetic Anomaly Signals Based on AlexNet Transfer Learning

doi: 10.11993/j.issn.2096-3920.2020.02.007
  • 1.

    Air Combat Service College, Naval Aviation University, Yantai 264001, China

  • 2.

    91550th Unit, the People’s Liberation Army of China, Dalian 116000, China

  • 3.

    91001th Unit, the People’s Liberation Army of China, Beijing 100080, China

  • Received Date: 2018-12-06
  • Rev Recd Date: 2019-01-04
  • Publish Date: 2020-04-30
    Abstract
  • Aiming at the problem in aviation antisubmarine that the magnetic anomaly signals of underwater targets are difficult to detect under low signal-to-noise ratio(SNR), a detection method of magnetic anomaly signals based on AlexNet transfer learning is proposed. Based on the principle of convolution neural network, a large number of signal sequences are obtained by simulating different situations of underwater targets and different flight routes and speeds of the aeromagnetic platform with Gaussian white noise, so as to simulate the signals measured by the magnetic detection platform. Then, the short-time Fourier transform(STFT) is applied to obtain the time-frequency diagram, and the time-frequency features are transferred and trained by using the AlexNet. Finally, the trained AlexNet deep convolutional neural network is tested by the test set. The results show that the AlexNet model has good generalization ability in the field of magnetic anomaly signal detection. When the SNR is -8 dB and the false alarm rate is 3%, the detection probability of magnetic anomaly signal reaches 93%.

     

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