Micro-Doppler-assisted Unmanned Aerial Vehicle Formation Detection Method in Urban Environments

ZHANG Jie; ZHU Yu; WANG Yang

doi:10.11999/JEIT240203

Volume 46 Issue 9

Sep. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(9): 3583-3591

ZHANG Jie, ZHU Yu, WANG Yang. Micro-Doppler-assisted Unmanned Aerial Vehicle Formation Detection Method in Urban Environments[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3583-3591. doi: 10.11999/JEIT240203

Citation:

ZHANG Jie, ZHU Yu, WANG Yang. Micro-Doppler-assisted Unmanned Aerial Vehicle Formation Detection Method in Urban Environments[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3583-3591. doi: 10.11999/JEIT240203

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Micro-Doppler-assisted Unmanned Aerial Vehicle Formation Detection Method in Urban Environments

doi: 10.11999/JEIT240203

ZHANG Jie^{1, 2},
ZHU Yu¹,
WANG Yang^{1
,
,}

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield S10 2TN, UK

Funds: The Natural Science Foundation of Chongqing (cstc2021jcyj-msxmX0634, CSTB2022NSCQ-MSX1125), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-K202300607), The Natural Science Foundation Innovation and Development Joint Fund Project of Chongqing (CSTB2022NSCQ-LZX0037)

Received Date: 2024-03-25
Rev Recd Date: 2024-07-22

Available Online: 2024-08-03

Publish Date: 2024-09-26

Abstract

Abstract

Considering the phenomena of complex electromagnetic environment, multipath clutter and dense interference signals in complex urban environments, the traditional Unmanned Aerial Vehicle(UAV) detection method extracts the target Doppler information for detection by obtaining echo signals, which is susceptible to environmental impacts and leads to unsatisfactory detection results. A micro-Doppler-assisted formation detection method for UAVs in urban environments is proposed in this paper, which makes full use of micro-motion characteristics to improve detection accuracy. Firstly, parametric modeling characterizes the radar echo micro-Doppler signals of UAV rotor blades in urban complex environments, and detects the micro-Doppler scintillation pulses by using YOLOv5s to effectively extract the positional information. Then, the Pulse Repetition Interval (PRI) transform of the radar signal sorting method is introduced to classify and obtain the number of UAV formations. Finally, K-means algorithm is utilized to verify the accuracy of the UAV formation detection method. The results show that the proposed method has a detection accuracy of more than 90% for seven UAVs at a signal-to-noise ratio of 2 dB, and can be used for UAV formation detection in urban complex environments where there are interfering pulses, multipath effects, and local pulse loss.
- Unmanned Aerial Vehicle(UAV) formation,
- Complex urban environments,
- Formation detection,
- YOLOv5s,
- Micro-doppler extraction

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References(16)

References

[1]	任智, 张栋, 唐硕, 等. 无人机集群反制与对抗技术探讨[J]. 指挥与控制学报, 2023, 9(6): 660–672. doi: 10.3969/j.issn.2096-0204.2023.06.0660. REN Zhi, ZHANG Dong, TANG Shuo, et al. Discussion on technologies of UAV swarm countermeasures and confrontation[J]. Journal of Command and Control, 2023, 9(6): 660–672. doi: 10.3969/j.issn.2096-0204.2023.06.0660.
[2]	于威, 侯学隆. 从纳卡冲突看无人机作战运用[J]. 舰船电子工程, 2022, 42(10): 8–12. doi: 10.3969/j.issn.1672-9730.2022.10.003. YU Wei and HOU Xuelong. Application of unmanned aerial vehicles in nagorno-karabakh conflict[J]. Ship Electronic Engineering, 2022, 42(10): 8–12. doi: 10.3969/j.issn.1672-9730.2022.10.003.
[3]	杨佳会, 朱超磊, 许佳. 俄乌冲突中的无人机运用[J]. 战术导弹技术, 2022(3): 116–123. doi: 10.16358/j.issn.1009-1300.20220081. YANG Jiahui, ZHU Chaolei, and XU Jia. Analysis of UAV deployment in Russia-Ukraine conflict[J]. Tactical Missile Technology, 2022(3): 116–123. doi: 10.16358/j.issn.1009-1300.20220081.
[4]	XU Chunbao, JIN Songpo, DING Zhoupeng, et al. Transmit beam control in low-altitude slow-moving small targets detection[C]. The 7th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi’an, China, 2022: 470–473. doi: 10.1109/ICSP54964.2022.9778645.
[5]	CHEN V C. Analysis of radar micro-Doppler with time-frequency transform[C]. The Tenth IEEE Workshop on Statistical Signal and Array Processing (Cat. No. 00TH8496), Pocono Manor, USA, 2000: 463–466. doi: 10.1109/SSAP.2000.870167.
[6]	MA Beili, CHEN Baixiao, ZHANG Zhengrong, et al. Classification of UAV and ground targets by micro-Doppler signatures based on PD surveillance radar[C]. 2021 CIE International Conference on Radar (Radar), Haikou, China, 2021: 1361–1365. doi: 10.1109/Radar53847.2021.10028361.
[7]	KANG K B, CHOI J H, CHO B L, et al. Analysis of micro-Doppler signatures of small UAVs based on Doppler spectrum[J]. IEEE Transactions on Aerospace and Electronic Systems, 2021, 57(5): 3252–3267. doi: 10.1109/TAES.2021.3074208.
[8]	JI Guangyu, SONG Chen, and HUO Hongtao. Detection and identification of low-slow-small rotor unmanned aerial vehicle using micro-Doppler information[J]. IEEE Access, 2021, 9: 99995–100008. doi: 10.1109/ACCESS.2021.3096264.
[9]	BJÖRKLUND S and WADSTRÖMER N. Target detection and classification of small drones by deep learning on radar micro-Doppler[C]. 2019 International Radar Conference (RADAR), Toulon, France, 2019: 1–6. doi: 10.1109/RADAR41533.2019.171294.
[10]	GAO Chengshuai, ZHOU Tao, WANG Bingqi, et al. Research on Anti Drone swarm technology based on complex urban environment[C]. 2023 International Conference on Network, Multimedia and Information Technology (NMITCON), Bengaluru, India, 2023: 1–6. doi: 10.1109/NMITCON58196.2023.10275854.
[11]	LV Ning and BAI Yunpeng. A UAV target recognition method based on micro-Doppler feature fusion[C]. The 3rd International Conference on Electronic Information Engineering and Computer Science (EIECS), Changchun, China, 2023: 970–973. doi: 10.1109/EIECS59936.2023.10435590.
[12]	张亚豪. 低空小型无人机雷达检测与识别[D]. [硕士论文], 哈尔滨工业大学, 2021. doi: 10.27061/d.cnki.ghgdu.2021.001396. ZHANG Yahao. Low altitude small drone radar detection and recognition[D]. [Master dissertation], Harbin Institute of Technology, 2021. doi: 10.27061/d.cnki.ghgdu.2021.001396.
[13]	DAHIROU Z and ZHENG Mao. Motion detection and object detection: Yolo (You Only Look Once)[C]. The 7th Annual International Conference on Network and Information Systems for Computers (ICNISC), Guiyang, China, 2021: 250–257. doi: 10.1109/ICNISC54316.2021.00053.
[14]	张怡霄, 郭文普, 康凯, 等. 基于数据场联合PRI变换与聚类的雷达信号分选[J]. 系统工程与电子技术, 2019, 41(7): 1509–1515. doi: 10.3969/j.issn.1001-506X.2019.07.11. ZHANG Yixiao, GUO Wenpu, KANG Kai, et al. Radar signal sorting method based on data field combined PRI transform and clustering[J]. Systems Engineering and Electronics, 2019, 41(7): 1509–1515. doi: 10.3969/j.issn.1001-506X.2019.07.11.
[15]	陈涛, 王天航, 郭立民. 基于PRI变换的雷达脉冲序列搜索方法[J]. 系统工程与电子技术. doi: 10.3969/j.issn.1001-506X.2017.06.12. CHEN Tao, WANG Tianghang, and GUO Limin. Sequence searching methods of radar signal pulses based on PRI transform algorithm[J]. Systems Engineering and Electronics. doi: 10.3969/j.issn.1001-506X.2017.06.12.
[16]	杨波, 孙闽红, 仇兆炀, 等. 基于YOLOv5与PRI变换的无人机编队检测[J]. 现代雷达, 2023, 45(9): 53–60. doi: 10.16592/j.cnki.1004-7859.2023.09.009. YANG Bo, SUN Minhong, QIU Zhaoyang, et al. UAV-formations detection based on YOLOv5 and PRI transformation[J]. Modern Radar, 2023, 45(9): 53–60. doi: 10.16592/j.cnki.1004-7859.2023.09.009.