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Article

Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

by 1,2 and 1,2,*
1
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China
2
Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Academic Editors: Fausto Pedro García Márquez, Mayorkinos Papaelias and Simone Marini
J. Mar. Sci. Eng. 2021, 9(6), 651; https://doi.org/10.3390/jmse9060651
Received: 6 May 2021 / Revised: 29 May 2021 / Accepted: 1 June 2021 / Published: 12 June 2021
(This article belongs to the Special Issue Artificial Intelligence in Marine Science and Engineering)
In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter. View Full-Text
Keywords: weak signal detection; CEEMD; IMF; LSTM weak signal detection; CEEMD; IMF; LSTM
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MDPI and ACS Style

Yan, Y.; Xing, H. Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network. J. Mar. Sci. Eng. 2021, 9, 651. https://doi.org/10.3390/jmse9060651

AMA Style

Yan Y, Xing H. Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network. Journal of Marine Science and Engineering. 2021; 9(6):651. https://doi.org/10.3390/jmse9060651

Chicago/Turabian Style

Yan, Yan, and Hongyan Xing. 2021. "Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network" Journal of Marine Science and Engineering 9, no. 6: 651. https://doi.org/10.3390/jmse9060651

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