大掠射角下幅相极化特征融合雷达导引头舰船检测方法

doi:10.12305/j.issn.1001-506X.2025.04.09

摘要/Abstract

摘要：

大掠射角下海面目标信号往往淹没在强海杂波中, 给雷达导引头目标检测带来了严峻的挑战。为改善大掠射角下雷达检测微弱目标性能, 提出一种基于多特征融合的海面目标检测方法。首先, 通过分析大掠射角下雷达导引头实测数据, 发现海杂波和舰船在展开相位差均值、同极化比相位均值和极差、同极化幅均比4个幅相极化特征方面存在较大差异。其次, 结合支持向量机方法, 与已有特征集对比, 验证了海杂波与舰船目标+海杂波在该四维特征空间可分性更高。进而, 对分类器加以虚警控制, 提出一种幅相特征融合的海面目标检测方法。最后, 利用实验数据验证了所提方法可以在大掠射角下实现雷达对海面目标的有效检测。

关键词: 目标检测, 海杂波, 大掠射角, 雷达导引头

Abstract:

The sea surface target signal at high grazing angle is often submerged in the strong sea clutter, which brings a severe challenge to the target detection of radar seeker. In order to improve the performance of radar detection of weak targets at high grazing angle, a target detection method of sea surface targets based on multi-feature fusion is proposed. Firstly, it is found that there are big differences between sea clutter and ship in four amplitude-phase polarization features, including the mean of unfolded phase difference, the mean and the difference of co-polarization ratio phase, and the ratio of average co-polarization amplitude, by analyzing the experimental data of radar seeker working at high grazing angle. Secondly, combined with the support vector machine (SVM) method, it is verified that the sea clutter and ship target with sea clutter are more separable in the four-dimensional feature space in comparison with the existing feature sets. Then the false alarm control is applied to the classifier, a sea surface targets detection method based on the fusion of amplitude and phase features is proposed. Finally, the experimental data are used to verify that the proposed method can effectively achieve the detection of sea surface targets at high grazing angle.

Key words: target detection, sea clutter, high grazing angle, radar seeker

中图分类号:

TN974

韩静雯, 杨勇, 连静, 王威. 大掠射角下幅相极化特征融合雷达导引头舰船检测方法[J]. 系统工程与电子技术, 2025, 47(4): 1127-1135.

Jingwen HAN, Yong YANG, Jing LIAN, Wei WANG. Ship detection method of amplitude-phase polarization feature fusion for radar seeker at high grazing angle[J]. Systems Engineering and Electronics, 2025, 47(4): 1127-1135.

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参数	特征集
参数	本文	文献[19]	文献[21]	文献[23]
平均检测概率	0.897 8	0.137 6	0.159 4	0.366 2

拟定虚警	训练虚警	测试虚警
0.10	0.091 4	0.109 4
0.05	0.053 2	0.058 1
0.01	0.010 6	0.009 7

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