Anti-swarm UAV radar system based on detection data fusion

doi:10.23919/JSEE.2023.000077

Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (5): 1167-1176.doi: 10.23919/JSEE.2023.000077

收稿日期:2023-01-29 接受日期:2023-05-09 出版日期:2024-10-18 发布日期:2024-11-06

Anti-swarm UAV radar system based on detection data fusion

Pengfei WANG¹^,²^,*(), Jinfeng HU¹^,³(), Wen HU⁴(), Weiguang WANG³(), Hao DONG³()

¹ School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
² China Airborne Missile Academy, Luoyang 471009, China
³ Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China
⁴ College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Received:2023-01-29 Accepted:2023-05-09 Online:2024-10-18 Published:2024-11-06
Contact: Pengfei WANG E-mail:66540555@qq.com;hujf@uestc.edu.cn;huwen@nuaa.edu.cn;13813018269@163.com;17351380098@nuaa.edu.cn
About author:
WANG Pengfei was born in 1982. He received his B.S. degree from Tianjin University in computer science and technology. He is a professor in China Airborne Missile Academy. His research interest is radar signal processing. E-mail: 66540555@qq.com

HU Jinfeng was born in 1976. He received his Ph.D. degree from University of Electronic Science and Technology in signal and information processing. He is a professor in University of Electronic Science and Technology of China. His research interest is radar signal processing. E-mail: hujf@uestc.edu.cn

HU Wen was born in 1979. He received his Ph.D. degree from Nanjing University of Science and Technology in communications engineering. He is a professor in Nanjing University of Aeronautics and Astronautics. His research interests is communications engineering. E-mail: huwen@nuaa.edu.cn

WANG Weiguang was born in 1994. He received his M.S. degree from Nanjing University of Aeronautics and Astronautics in communication and information systems. His research interest is signal and data processing. E-mail: 13813018269@163.com

DONG Hao was born in 1998. He received his M.S. degree from Nanjing University of Aeronautics and Astronautics, in communications engineering. His research interest is signal and data processing. E-mail: 17351380098@nuaa.edu.cn
Supported by:
This work was supported by the Municipal Gavemment of Quzhou (2022D0009;2022D013;2022D033) and the Science and Technology Project of Sichuan Province (2023YFG0176)

摘要/Abstract

Abstract:

There is a growing body of research on the swarm unmanned aerial vehicle (UAV) in recent years, which has the characteristics of small, low speed, and low height as radar target. To confront the swarm UAV, the design of anti-UAV radar system based on multiple input multiple output (MIMO) is put forward, which can elevate the performance of resolution, angle accuracy, high data rate, and tracking flexibility for swarm UAV detection. Target resolution and detection are the core problem in detecting the swarm UAV. The distinct advantage of MIMO system in angular accuracy measurement is demonstrated by comparing MIMO radar with phased array radar. Since MIMO radar has better performance in resolution, swarm UAV detection still has difficulty in target detection. This paper proposes a multi-mode data fusion algorithm based on deep neural networks to improve the detection effect. Subsequently, signal processing and data processing based on the detection fusion algorithm above are designed, forming a high resolution detection loop. Several simulations are designed to illustrate the feasibility of the designed system and the proposed algorithm.

Key words: swarm, radar, high resolution, deep neural network, fusion algorithm

. [J]. Journal of Systems Engineering and Electronics, 2024, 35(5): 1167-1176.

Pengfei WANG, Jinfeng HU, Wen HU, Weiguang WANG, Hao DONG. Anti-swarm UAV radar system based on detection data fusion[J]. Journal of Systems Engineering and Electronics, 2024, 35(5): 1167-1176.

图/表 15

参考文献 18

1	ZHAO T F, XU S, QU Y, et al luster-based algorithm of reconnaissance UAV swarm based on wireless ultraviolet secret communication. Journal of Electronics & Information Technology, 2019, 41 (4): 967- 972.
2	ZHOU Y K, RAO B, WANG W UAV swarm intelligence: recent advances and future trends. IEEE Access, 2020, 8, 183856- 183878. doi: 10.1109/ACCESS.2020.3028865
3	CHEN P, ZHENG L, WANG X D, et al Moving target detection using colocated MIMO radar on multiple distributed moving platforms. IEEE Trans. on Signal Processing, 2017, 65 (17): 4670- 4683. doi: 10.1109/TSP.2017.2714999
4	COHEN D, ELDAR Y C, HAIMOVICHSUMMER A M Sub-Nyquist MIMO radar. IEEE Trans. on Signal Processing, 2018, 66 (16): 4315- 4330. doi: 10.1109/TSP.2018.2838541
5	SHI J P, HU G P, LE T DOA estimation algorithms for low-angle targets with MIMO radar. Electronics Letters, 2016, 52 (8): 652- 654. doi: 10.1049/el.2015.3355
6	TANG B, TUCK J, STOICA P Polyphase waveform design for MIMO radar space time adaptive processing. IEEE Trans. on Signal Processing, 2020, 68, 2143- 2154. doi: 10.1109/TSP.2020.2983833
7	ZHOU J W, LI H B, CUI W Low-complexity joint transmit and receive beamforming for MIMO radar with multi-targets. IEEE Signal Processing Letters, 2020, 27, 1410- 1414. doi: 10.1109/LSP.2020.3015104
8	DELIGIANNIS A, LAMBOTHARAN S, CHAMBERS J A Game theoretic analysis for MIMO radars with multiple targets. IEEE Trans. on Aerospace and Electronic Systems, 2016, 52 (6): 2760- 2774. doi: 10.1109/TAES.2016.150699
9	SHEN F F, LI Y X, ZHAO G H, et al High resolution angle estimation of the bistatic MIMO radar. Journal of Xidian University, 2013, 40 (5): 33- 37, 106.
10	JIAO S J, LIU R, WANG B Q, et al Operational effectiveness evaluation of radar detecting “swarm” unmanned aerial vehicle. Shipboard Electronic Countermeasure, 2019, 42 (1): 44- 47.
11	NIJSURE Y A, KADDOUM G, KHADDAJ MALLAT N, et al Cognitive chaotic UWB-MIMO detect-avoid radar for autonomous UAV navigation. IEEE Trans. on Intelligent Transportation Systems, 2016, 17 (11): 3121- 3131. doi: 10.1109/TITS.2016.2539002
12	ALAEE-KERAHROODI M, MISHRA K V, MR B S. Radar beampattern design for a drone swarm. Proc. of the 53rd Asilomar Conference on Signals, Systems and Computers, 2019: 1416−1421.
13	KEMKEMIAN S, NOUVEL-FIANI M, CORNI C P, et al. A MIMO radar for sense and avoid function: a fully static solution for UAV. Proc. of the 11th International Radar Symposium, 2010: 1−4.
14	MOLCHANOV P A, CONTARINO V M. New distributed radar technology based on UAV or UGV application. Proceedings of the SPIE, 2013. DOI: 10.1117/12.2016379.
15	YU X X, ALHUJAILI K, CUI G L, et al MIMO radar waveform design in the presence of multiple targets and practical constraints. IEEE Trans. on Signal Processing, 2020, 68, 1974- 1989. doi: 10.1109/TSP.2020.2979602
16	YANG T. Study on waveform design and real-time processing system for MIMO radar. Xi’an: Xidian University, 2014. (in Chinese)
17	ZHANG Y C, SHUI P L Improved probabilistic multi-hypothesis tracker via the Poission point process. Journal of Xidian University, 2021, 48 (2): 27- 34.
18	CORALUPPI S P, RAGO C, CARTHEL C A. Multiple-hypothesis group tracking. Proc. of the IEEE Radar Conference, 2020. DOI: 10.1109/RadarConf2043947.2020.9266285.

Anti-swarm UAV radar system based on detection data fusion

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 15

参考文献 18

相关文章 0

编辑推荐

Metrics

本文评价