Sea Clutter Spectral Parameters Prediction and Influence Factor Analysis Based on Deep Learning

ZHANG Yushi; LI Xiaoyu; ZHANG Jinpeng; XIA Xiaoyun

doi:10.12000/JR22133

Volume 12 Issue 1

Feb. 2023

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Article Navigation > Journal of Radars > 2023 > 12(1): 110-119

Jin Tian, Song Yongping. Sparse Imaging of Building Layouts in Ultra-wideband Radar[J]. Journal of Radars, 2018, 7(3): 275-284. doi: 10.12000/JR18031

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ZHANG Yushi, LI Xiaoyu, ZHANG Jinpeng, et al. Sea clutter spectral parameters prediction and influence factor analysis based on deep learning[J]. Journal of Radars, 2023, 12(1): 110–119. doi: 10.12000/JR22133

Jin Tian, Song Yongping. Sparse Imaging of Building Layouts in Ultra-wideband Radar[J]. Journal of Radars, 2018, 7(3): 275-284. doi: 10.12000/JR18031

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Sea Clutter Spectral Parameters Prediction and Influence Factor Analysis Based on Deep Learning

DOI: 10.12000/JR22133

National Key Laboratory of Electromagnetic Environment, China Research Institute of Radiowave Propagation, Qingdao 266107, China

Funds: The National Natural Science Foundation of China (U2006207)

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Corresponding author: ZHANG Jinpeng, zhangjp@crirp.ac.cn
Received Date: 2022-07-01
Rev Recd Date: 2022-09-05

Available Online: 2022-09-09

Publish Date: 2022-09-27

Abstract

Abstract

Using Deep Neural Network (DNN) modeling technology, a prediction model of Doppler spectral parameters of sea clutter based on multiple measurement conditions is established based on measured data of sea clutter from shore-based radar under different radar parameters and marine environmental parameters. The recognition of sea clutter spectral characteristics based on environmental characteristics and independent of clutter data is realized. The spectral frequency shift and broadening prediction accuracy are greater than 90%. Based on the prediction model, an analysis method of Doppler spectrum influence factors based on the parameter cycle decreasing cognition is proposed. The influence of different measurement parameters on the Doppler spectrum prediction of sea clutter is analyzed, and the change law of spectrum parameters with the main influence factors is obtained. The results are of great significance to the application of sea surface target detection based on Doppler characteristics.
- Sea clutter,
- Doppler spectrum,
- Deep Neural Network (DNN),
- Influence factors,
- Prediction model

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References(19)

References

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