无人机雷达航迹运动特征提取及组合分类方法

doi:10.12305/j.issn.1001-506X.2023.10.16

系统工程与电子技术 ›› 2023, Vol. 45 ›› Issue (10): 3122-3131.doi: 10.12305/j.issn.1001-506X.2023.10.16

• 系统工程 • 上一篇

无人机雷达航迹运动特征提取及组合分类方法

刘佳¹, 徐群玉²^,*, 陈唯实³

1. 北京航空航天大学前沿科学技术创新研究院, 北京 100191
2. 中国民航科学技术研究院民航法规与标准化研究所, 北京 100028
3. 中国民航科学技术研究院机场所, 北京 100028

收稿日期:2021-09-18 出版日期:2023-09-25 发布日期:2023-10-11
通讯作者: 徐群玉
作者简介:刘佳(1985—), 男, 副研究员, 博士, 主要研究方向为雷达目标特性建模、雷达目标识别算法、计算电磁学
徐群玉(1985—), 女, 助理研究员, 博士, 主要研究方向为机场安全监视、目标检测与识别、民航标准化体系与技术研究
陈唯实(1982—), 男, 研究员, 博士, 主要研究方向为低空空域安全监视、雷达目标检测与跟踪、机场安全运行技术
基金资助:
国家自然科学基金委员会-中国民航局民航联合研究基金(U1933135);国家自然科学基金委员会-中国民航局民航联合研究基金(U1633122);北航卓越百人计划-双一流引导专项(ZG216S2182)

Motion feature extraction and ensembled classification method based on radar tracks for drones

Jia LIU¹, Qunyu XU²^,*, Weishi CHEN³

1. Research Institute for Frontier Science, Beihang University, Beijing 100191, China
2. 2 Research Institute of Civil Aviation Law, China Academy of Civil Aviation Science and Technology, Beijing 100028, China
3. Airport Research Institute, China Academy of Civil Aviation Science and Technology, Beijing 100028, China

Received:2021-09-18 Online:2023-09-25 Published:2023-10-11
Contact: Qunyu XU

摘要/Abstract

摘要：

飞鸟和无人机目标的雷达回波存在高度相似性, 区分难度较大。因此, 对无人机、飞鸟以及动态降水杂波形成的目标航迹的时空间特征进行了研究, 分析了无人机和飞鸟在运动机理以及行为模式上的差异, 提出了一种基于目标航迹的运动特征提取方法，并构建了目标特征向量。基于探鸟雷达系统提供的目标实测航迹数据, 建立了训练和测试样本集, 采用监督类学习方法并结合随机森林模型实现了对无人机、飞鸟和降水杂波目标航迹的区分。实验结果表明，在广域范围内，无人机目标的正确识别率可达85%以上, 分类器模型的运算效率高, 样本适应性强, 具备较好的普适性和实用价值。

关键词: 无人机检测, 雷达目标识别, 特征提取, 监督类学习

Abstract:

The radar echoes of birds and drones target have high similarity, which make it difficult to distinguish them. Therefore, the spatio-temporal characteristics of target tracks formed by drones, birds and dynamic precipitation clutter are studied, and the differences in motion mechanisms and behavior patterns between drones and birds are analyzed. A motion feature extraction method based on target tracks is proposed and target feature vectors are constructed. Based on the measured track data of the target provided by the detection bird radar system, a training and test sample set is established. The supervised learning method combined with the random forest model is used to distinguish the target tracks of drones, birds and precipitation clutter. The experimental results show that the correct recognition rate of drone targets over a wide area can reach over 85%, and the classifier model has high calculation efficiency, strong sample adaptability, and good universality and practical value.

Key words: drone detection, radar target recognition, feature extraction, supervised learning

中图分类号:

TN959.1

刘佳, 徐群玉, 陈唯实. 无人机雷达航迹运动特征提取及组合分类方法[J]. 系统工程与电子技术, 2023, 45(10): 3122-3131.

Jia LIU, Qunyu XU, Weishi CHEN. Motion feature extraction and ensembled classification method based on radar tracks for drones[J]. Systems Engineering and Electronics, 2023, 45(10): 3122-3131.

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震荡次数	权值
1	1
2	1.5
3	2
4	3
大于等于5	5

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无人机雷达航迹运动特征提取及组合分类方法

Motion feature extraction and ensembled classification method based on radar tracks for drones

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图/表 16

参考文献 30

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目标类型	飞鸟	无人机	降水
飞鸟	92	5	3
无人机	11	89	0
降水	14	0	86

方法	飞鸟	无人机
本文方法	87	85
文献[19]方法	79	76