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Passive tracking of underwater acoustic targets based on multi-beam LOFAR and deep learning

Author

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  • Maofa Wang
  • Baochun Qiu
  • Zefei Zhu
  • Li Ma
  • Chuanping Zhou

Abstract

Conventional passive tracking methods for underwater acoustic targets in sonar engineering generate time azimuth histogram and use it as a basis for target azimuth and tracking. Passive underwater acoustic targets only have azimuth information on the time azimuth histogram, which is easy to be lost and disturbed by ocean noise. To improve the accuracy of passive tracking, we propose to adopt the processed multi-beam Low Frequency Analysis and Recording (LOFAR) as the dataset for passive tracking. In this paper, an improved LeNet-5 convolutional neural network model (CNN) model is used to identify targets, and a passive tracking method for underwater acoustic targets based on multi-beam LOFAR and deep learning is proposed, combined with Extended Kalman Filter (EKF) to improve the tracking accuracy. The performance of the method under realistic conditions is evaluated through simulation analysis and validation using data obtained from marine experiments.

Suggested Citation

  • Maofa Wang & Baochun Qiu & Zefei Zhu & Li Ma & Chuanping Zhou, 2022. "Passive tracking of underwater acoustic targets based on multi-beam LOFAR and deep learning," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-24, December.
  • Handle: RePEc:plo:pone00:0273898
    DOI: 10.1371/journal.pone.0273898
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