全斯农^1,,
范晖^{1, 2,,},
代大海¹,
王威^3,,
肖顺平¹,
王雪松¹
1.国防科技大学电子科学学院电子信息系统复杂环境效应国家重点实验室 长沙 410073
2.中南林业科技大学计算机与信息工程学院 长沙 410004
3.国防科技大学电子科学学院 长沙 410073
基金项目:国家自然科学基金青年科学基金(62001487)；湖南科学委员会****基金(2017JJ1006)

详细信息

作者简介:全斯农(1991–)，男，湖南人，博士，国防科技大学电子科学学院讲师。主要研究方向为雷达极化信息处理、极化目标检测与识别。E-mail: qsnong@hotmail.com
范晖：范　晖(1985–)，女，湖北人，博士在读，中南林业科技大学讲师。主要研究方向为极化SAR图像解译、目标分类识别。E-mail: fh_luckygirl@163.com
王威：王　威(1989–)，男，安徽人，博士，国防科技大学电子科学学院特聘副研究员。主要研究方向为极化SAR信息处理、雷达成像、目标检测与识别等。E-mail: wangwei_nudt@hotmail.com

通讯作者:范晖 fh_luckygirl@163.com

责任主编：杨健 Corresponding Editor: YANG Jian
中图分类号:TN957.52

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出版历程

收稿日期:2020-09-01
修回日期:2020-10-22
网络出版日期:2020-11-16

Recognition of Ships and Chaff Clouds Based on Sophisticated Polarimetric Target Decomposition

QUAN Sinong^1,,
FAN Hui^{1, 2,,},
DAI Dahai¹,
WANG Wei^3,,
XIAO Shunping¹,
WANG Xuesong¹
1. The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, China
2. College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China
3. College of Electronic Science, National University of Defense Technology, Changsha 410073, China
Funds:Science Foundation for Youth of The National Natural Science Foundation of China (62001487), Outstanding Youth Fund of Hunan Science Committee (2017JJ1006)

More Information

Corresponding author:FAN Hui, fh_luckygirl@163.com

摘要
摘要:用于干扰舰船目标的箔条云通常具有与舰船目标相近的尺寸和雷达散射截面积，这使得舰船与箔条云的识别成为一个非常有挑战性的问题。该文提出一种基于精细极化目标分解的识别方法。为了能够有效地识别舰船目标与箔条云，该文首先结合3种精细化散射模型，提出了一种基于精细散射模型的七成分分解方法。通过这种分解方法可以有效地刻画舰船目标的散射特性。为了将舰船与箔条云的极化特性进行有效的对比和区分，该文根据分解得到的散射成分贡献构造了一个稳健的散射贡献差特征。最后，通过将构造的散射贡献差与极化散射角结合，构造了新的特征矢量并利用支持向量机实现了最终的识别。实验利用仿真和实测的极化雷达数据对所提方法进行了验证，结果表明该方法优于现有的其他方法，并能够达到最高98%的正确识别率。
关键词:舰船识别/
箔条云干扰/
精细化极化分解
Abstract:The recognition of ships from chaff cloud jamming is challenging because they have similar dimensions and radar cross sections. In this paper, we propose a polarimetric recognition technique with sophisticated polarimetric target decomposition. Three sophisticated scattering models are integrated to constitute a seven-component model-based decomposition method so as to accurately characterize the dominant and local scattering of ships. Based on the concepts of contrast and suppression, a robust scattering contribution difference feature is designed according to the derived scattering contributions. The constructed feature vector, combined with the polarization scattering angle, is inputted into the support vector machine to fulfill the recognition process. Simulated and real polarimetric radar data are utilized to test the proposed method, and the results show that the proposed method outperforms state-of-the-art methods by achieving the highest recognition rate of over 98%.
Key words:Ship recognition/
Chaff cloud jamming/
Sophisticated polarimetric decomposition



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