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Open AccessArticle

Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network

1
School of Electronic Engineering, Xidian University, Xi'an 710071, China
2
China Research Institute of Radiowave Propagation, Qingdao 266107, China
3
School of Physical and Optoelectronic Engineering, Xidian University, Xi'an 710071, China
4
Department of Embedded System Engineering, Incheon National University, Incheon 22012, Korea
5
Xi'an Brain-Like Perception Technology Development Co., Ltd., Xi’an 710071, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2826; https://doi.org/10.3390/rs11232826
Received: 24 August 2019 / Revised: 14 November 2019 / Accepted: 22 November 2019 / Published: 28 November 2019
In the marine environment, shore-based radars play an important role in military surveillance and sensing. Sea clutter is one of the main factors affecting the performance of shore-based radar. Affected by marine environmental factors and radar parameters, the fluctuation law of sea clutter amplitude is very complicated. In the process of training a sea clutter amplitude prediction model, the traditional method updates the model parameters according to the current input data and the parameters in the current model, and cannot utilize the historical information of sea clutter amplitude. It is only possible to learn the short-term variation characteristics of the sea clutter. In order to learn the long-term variation law of sea clutter, a sea clutter prediction system based on the long short-term memory neural network is proposed. Based on sea clutter data collected by IPIX radar, UHF-band radar and S-band radar, the experimental results show that the mean square error of this prediction system is smaller than the traditional prediction methods. The sea clutter suppression signal is extracted by comparing the predicted sea clutter data with the original sea clutter data. The results show that the proposed sea clutter prediction system has a good effect on sea clutter suppression. View Full-Text
Keywords: shore-based radars; sea clutter; long short-term memory neural network; nonlinear prediction; sea clutter suppression shore-based radars; sea clutter; long short-term memory neural network; nonlinear prediction; sea clutter suppression
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Ma, L.; Wu, J.; Zhang, J.; Wu, Z.; Jeon, G.; Tan, M.; Zhang, Y. Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network. Remote Sens. 2019, 11, 2826.

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