陈小龙^1,,,
陈唯实^2,,
饶云华^3,,
黄勇^1,,
关键^1,,
董云龙^1,
1.海军航空大学 烟台 264001
2.中国民航科学技术研究院机场研究所 北京 100028
3.武汉大学电子信息学院 武汉 430072
基金项目:国家自然科学基金(U1933135, 61871391, 61931021)，山东省重点研发计划(2019GSF111004)，基础加强计划技术领域基金(2102024)，装发“十三五”领域基金(61404130212)

详细信息

作者简介:陈小龙(1985–)，男，山东烟台人，博士，副教授。主要研究方向为雷达动目标检测、海杂波抑制、雷达信号精细化处理等。获中国专利优秀奖、军队科技进步一等奖、中国产学研合作促进会军民融合奖、中国电子学会/全军优博。入选中国科协青托，第七届中国电子学会优秀科技工作者，世界无线电联盟、国际应用计算电磁学会青年科学家奖。E-mail: cxlcxl1209@163.com
陈唯实(1982–)，男，天津人，博士，研究员。主要研究方向为机场运行安全管理、无人机反制、鸟击防范、低空空域监视等。E-mail: chenwsh@mail.castc.org.cn
饶云华(1972–)，男，博士，副教授，主要研究方向为新体制雷达，雷达系统设计与信号处理等。E-mail: ryh@whu.edu.cn
黄勇：黄　勇(1979–)，男，副教授，主要研究方向为雷达目标检测、多维信号处理等。E-mail: huangyong_2003@163.com
关键：关　键(1968–)，男，辽宁锦州人，教授，博士生导师。主要研究方向为雷达目标检测与跟踪、侦察图像处理和信息融合。E-mail: guanjian_68@163.com
董云龙(1974–)，男，天津宝坻人，教授，主要研究方向为多传感器信息融合。E-mail: china_dyl@sina.com

通讯作者:陈小龙 cxlcxl1209@163.com

责任主编：张群 Corresponding Editor: ZHANG Qun
中图分类号:TN957.51

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出版历程

收稿日期:2020-05-27
修回日期:2020-06-16
网络出版日期:2020-07-02

Progress and Prospects of Radar Target Detection and Recognition Technology for Flying Birds and Unmanned Aerial Vehicles (in English)

CHEN Xiaolong^1,,,
CHEN Weishi^2,,
RAO Yunhua^3,,
HUANG Yong^1,,
GUAN Jian^1,,
DONG Yunlong^1,
1. Naval Aviation University, Yantai 264001, China
2. Airport Research Institute, China Academy of Civil Aviation Science and Technology, Beijing 100028, China
3. School of Electronic Information, Wuhan University, Wuhan 430072, China
Funds:The National Natural Science Foundation of China (NSFC) (U1933135, 61871391, 61931021), Key Research and Development Program of Shandong (2019GSF111004), Fundamental Strengthening Technology Program (2102024), Foundation of the Equipment development of the “13th Five-Year Plan” (61404130212)

More Information

Corresponding author:CHEN Xiaolong, cxlcxl1209@163.com

摘要
摘要:飞鸟和无人机(UAVs)是典型的“低慢小”目标，具有低可观测性，对两者的有效监视和识别成为保障空中航路安全、城市安保等需求迫切需要解决的难题。飞鸟和无人机目标类型多、飞行高度低、机动性强、雷达散射截面积小，加之探测环境复杂，给目标探测带来极大困扰，已成为世界性难题。因此迫切需要研发“看得见(检测能力强)、辨得明(识别概率高)”的无人机、飞鸟等“低慢小”目标监视手段和技术，实现目标的精细化描述和识别。该文集中对近年来复杂场景下旋翼无人机和飞鸟目标检测与识别技术的研究进展进行了归纳总结，介绍了飞鸟和无人机探测的主要手段，从回波建模和微动特性认知、泛探模式下机动特征增强与提取、分布式多视角特征融合、运动轨迹差异、深度学习智能分类等方面给出了检测和识别的有效途径。最后，该文总结了现有研究存在的问题，对未来复杂场景下飞鸟和无人机目标检测与识别技术的发展进行了展望。
关键词:雷达目标检测/
飞鸟和无人机/
微多普勒/
特征提取/
目标识别/
深度学习
Abstract:Flying birds and Unmanned Aerial Vehicles (UAVs) are typical “low, slow, and small” targets with low observability. The need for effective monitoring and identification of these two targets has become urgent and must be solved to ensure the safety of air routes and urban areas. There are many types of flying birds and UAVs that are characterized by low flying heights, strong maneuverability, small radar cross-sectional areas, and complicated detection environments, which are posing great challenges in target detection worldwide. “Visible (high detection ability) and clear-cut (high recognition probability)” methods and technologies must be developed that can finely describe and recognize UAVs, flying birds, and “low-slow-small” targets. This paper reviews the recent progress in research on detection and recognition technologies for rotor UAVs and flying birds in complex scenes and discusses effective detection and recognition methods for the detection of birds and drones, including echo modeling and recognition of fretting characteristics, the enhancement and extraction of maneuvering features in ubiquitous observation mode, distributed multi-view features fusion, differences in motion trajectories, and intelligent classification via deep learning. Lastly, the problems of existing research approaches are summarized, and we consider the future development prospects of target detection and recognition technologies for flying birds and UAVs in complex scenarios.
Key words:Radar target detection/
Flying bird and Unmanned Aerial Vehicle (UAV) target/
Micro-Doppler/
Features extraction/
Target recognition/
Deep learning



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