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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
[1] SHI Yanling, GUO Yaxing, YAO Tingting, et al. Sea-surface small floating target recurrence plots FAC classification based on CNN[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5115713. doi: 10.1109/TGRS.2022.3192986[2] QU Qizhe, WANG Yongliang, LIU Weijian, et al. A false alarm controllable detection method based on CNN for sea-surface small targets[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4025705. doi: 10.1109/LGRS.2022.3190865[3] WARD K D, TOUGH R J A, and WATTS S. Sea Clutter: Scattering, The K Distribution and Radar Performance[M]. 2nd ed. London: The Institution of Engineering and Technology, 2013: 129–142.[4] 李清亮, 尹志盈, 朱秀芹, 等. 雷达地海杂波测量与建模[M]. 北京: 国防工业出版社, 2017: 312–408.LI Qingliang, YIN Zhiying, ZHU Xiuqin, et al. Measurement and Modeling of Radar Clutter from Land and Sea[M]. Beijing: National Defense Industry Press, 2017: 312–408.[5] 丁昊, 董云龙, 刘宁波, 等. 海杂波特性认知研究进展与展望[J]. 雷达学报, 2016, 5(5): 499–516. doi: 10.12000/JR16069DING Hao, DONG Yunlong, LIU Ningbo, et al. Overview and prospects of research on sea clutter property cognition[J]. Journal of Radars, 2016, 5(5): 499–516. doi: 10.12000/JR16069[6] CROMBIE D D. Doppler spectrum of sea echo at 13.56 Mc./s.[J]. Nature, 1955, 175(4459): 681–682. doi: 10.1038/175681a0[7] LEE P H Y, BARTER J D, BEACH K L, et al. Power spectral lineshapes of microwave radiation backscattered from sea surfaces at small grazing angles[J]. IEE Proceedings-Radar, Sonar and Navigation, 1995, 142(5): 252–258. doi: 10.1049/ip-rsn:19952084[8] WALKER D. Experimentally motivated model for low grazing angle radar Doppler spectra of the sea surface[J]. IEE Proceedings-Radar, Sonar and Navigation, 2000, 147(3): 114–120. doi: 10.1049/ip-rsn:20000386[9] ROSENBERG L and STACY N J. Analysis of medium grazing angle X-band sea-clutter Doppler spectra[C]. 2008 IEEE Radar Conference, Rome, Italy, 2008: 1–6.[10] ROSENBERG L. Parametric modeling of sea clutter Doppler spectra[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5105409. doi: 10.1109/TGRS.2021.3107950[11] ZHANG Jinpeng, ZHANG Yushi, XU Xinyu, et al. Estimation of sea clutter inherent Doppler spectrum from shipborne S-band radar sea echo[J]. Chinese Physics B, 2020, 29(6): 068402. doi: 10.1088/1674-1056/ab888a[12] WEN Liwu, DING Jinshan, ZHONG Chao, et al. Modeling of correlated complex sea clutter using unsupervised phase retrieval[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(1): 228–239. doi: 10.1109/TGRS.2020.2995892[13] MCDONALD M K and CERUTTI-MAORI D. Clairvoyant radar sea clutter covariance matrix modelling[J]. IET Radar, Sonar & Navigation, 2017, 11(1): 154–160. doi: 10.1049/iet-rsn.2016.0103[14] SHEN Yan and LI Guoqiang. The chaotic neural network is used to predict the sea clutter signal[C]. 2009 International Conference on Artificial Intelligence and Computational Intelligence, Shanghai, China, 2009: 25–30.[15] FERNÁNDEZ J R M and DE LA CONCEPCIÓN BACALLAO VIDAL J. Improved shape parameter estimation in K clutter with neural networks and deep learning[J]. International Journal of Interactive Multimedia and Artificial Intelligence, 2016, 3(7): 96–103. doi: 10.9781/ijimai.2016.3714[16] MA Liwen, WU Jiaji, ZHANG Jinpeng, et al. Sea clutter amplitude prediction using a Long short-term memory neural network[J]. Remote Sensing, 2019, 11(23): 2826. doi: 10.3390/rs11232826[17] SHUI Penglang, SHI Xiaofan, LI Xin, et al. GRNN-based predictors of UHF-band sea clutter reflectivity at low grazing angle[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 1502205. doi: 10.1109/LGRS.2021.3076842[18] 张晓峰, 王莉, 殷国东, 等. Ku波段地海杂波极化特性实验与分析[J]. 电波科学学报, 2019, 34(6): 676–686. doi: 10.13443/j.cjors.2019043003ZHANG Xiaofeng, WANG Li, YIN Guodong, et al. The experiments and analysis of polarization characteristics of the ground and sea clutters at Ku band[J]. Chinese Journal of Radio Science, 2019, 34(6): 676–686. doi: 10.13443/j.cjors.2019043003[19] MADSEN K, NIELSEN H B, and TINGLEFF O. Methods for Non-Linear Least Squares Problems[M]. 2nd ed. Lyngby: Informatics and Mathematical Modelling, Technical University of Denmark, 2004: 24–29. -
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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