An airborne radar sea clutter spectrum parameters estimation method based on intelligent learning

被引：0

作者：

Fan, Yifei ^{[1
]}

Wang, Xinbao ^{[1
]}

Su, Jia ^{[1
]}

Tao, Mingliang ^{[1
]}

Chen, Ming ^{[2
]}

Wang, Ling ^{[1
]}

机构：

[1] School of Electronics and Information, Northwestern Polytechnical University, Xi′an

[2] Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou

来源：

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University | 2024年 / 42卷 / 03期

关键词：

deep learning; doppler characteristics; parameters estimation; sea clutter;

D O I：

10.1051/jnwpu/20244230446

中图分类号：

学科分类号：

摘要：

Traditional airborne radar sea clutter suppression methods have a high degree of human participation and large errors in estimating the clutter power spectrum. With the development of modern signal processing and artificial intelligence, deep learning methods are used to study the sea clutter more quickly and intelligently. This paper proposes an airborne radar sea clutter spectrum parameter estimation method based on intelligent learning. It establishes a sea clutter training model based on the one-dimensional LeNet-5. Then the simulated and measured sea clutter data are input into the trained model to estimate the center and width of the power spectrum, thus realizing the direct perception of the sea clutter spectrum characteristics. The experimental results show that the proposed method has a higher estimation accuracy and better robustness than the traditional methods. ©2024 Journal of Northwestern Polytechnical University.

引用

页码：446 / 452

页数：6

共 11 条

[1] DUK V, ROSENBERG L, NG B W H., Target detection in sea-clutter using stationary wavelet transforms, IEEE Trans on Aerospace and Electronic Systems, 53, 3, pp. 1136-1146, (2017)
[2] ROSENBERG L, WATTS S., Coherent detection in medium grazing angel sea-clutter, IET Rader, Sonar and Navigation, 11, 9, pp. 1340-1348, (2017)
[3] LIU Ningbo, JIANG Xingyu, DING Hao, Et al., Summary of research on characteristics of radar sea clutter and target detection at high grazing angles, Journal of Electronics & Information Technology, 43, 10, pp. 2771-2780, (2021)
[4] FAN Y, LI C, LI D, Et al., A novel sea clutter suppression method based on neural network, 2021 IEEE 4th International Conference on Electronic Information and Communication Technology, pp. 437-440, (2021)
[5] ZHANG Yushi, LI Xiaoyu, ZHANG Jinpeng, Et al., Sea clutter spectral parameters prediction and influence factor analysis based on deep learning, Journal of Radars, 12, 1, pp. 110-119, (2023)
[6] ZOU Zhengxia, SHI Zhenwei, Ship detection in spaceborne optical image with SVD networks, IEEE Trans on Geoscience and Remote Sensing, 54, 10, pp. 5832-5845, (2016)
[7] FERNANDEZ J R M, VIDAL J D L C B., Improved shape parameter estimation in K clutter with neural networks and deep learning, International Journal of Interactive Multimedia and Artificial Intelligence, 3, 7, pp. 96-103, (2016)
[8] MA Liwen, WU Jiaji, ZHANG Jinpeng, Et al., Sea clutter amplitude prediction using a long short-term memory neural network, Remote Sensing, 11, (2019)
[9] SHUI Penglang, SHI Xiaofan, LI Xin, Et al., GRNN-Based predictors of UHF-band sea clutter reflectivity at low grazing angle, IEEE Geoscience and Remote Sensing Letters, 19, pp. 1-5, (2022)
[10] HUANG Penghui, ZOU Zihao, LIU Xingzhao, Et al., Robust sea clutter suppression method for multichannel airborne radar, Journal of Electronics & Information Technology, 43, 9, pp. 2680-2687, (2021)

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