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| Citation: | 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
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Sea Clutter Spectral Parameters Prediction and Influence Factor Analysis Based on Deep Learning
DOI: 10.12000/JR22133
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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. -
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References
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- Figure 1. Reality of Ku band sea clutter measurement radar
- Figure 2. Extraction process of Doppler spectral parameters of sea clutter
- Figure 3. Relationship between wind, wave and current direction in test data
- Figure 4. Prediction model framework of sea clutter spectral parameters
- Figure 5. DNN-5 network structure diagram
- Figure 6. Scatter plot of spectral parameter prediction based on DNN-5 model
- Figure 7. Error distribution of Doppler parameter prediction based on DNN-5 model
- Figure 8. Influence analysis process of spectral parameters based on cyclic decreasing cognition
- Figure 9. Statistical chart of influence factors of Doppler parameters
- Figure 10. Fitting diagram of change trend of main influencing factors