A multi target intention recognition model of drones based on transfer learning

doi:10.23919/JSEE.2025.000137

Journal of Systems Engineering and Electronics ›› 2025, Vol. 36 ›› Issue (5): 1247-1258.doi: 10.23919/JSEE.2025.000137

• SYSTEMS ENGINEERING • Previous Articles

A multi target intention recognition model of drones based on transfer learning

Shichang WAN¹^,²(), Hao LI³^,*(), Yahui HU³(), Xuhua WANG¹(), Siyuan CUI³()

¹ School of Computer Science and Technology, Xidian University, Xi’an 710071, China
² School of Mathematics and Computer Application, Shangluo University, Shangluo 726000, China
³ Air Force Early Warning Academy, Wuhan 430010, China

Received:2024-04-30 Online:2025-10-18 Published:2025-10-24
Contact: Hao LI E-mail:wanshichang@126.com;afeu_li@163.com;hyh5800@163.com;daleiwxh@163.com;zcehy07@163.com
About author:
WAN Shichang was born in 1984. He received his B.S. degree in information and computing science from Yunnan University, Kunming, China, in 2007 and M.S. degree in computer software and theory from Shaanxi Normal University, Xi’an, China, in 2010. He is currently pursuing his Ph.D. degree in the School of Computer Science and Technology, Xidian University. His research interests include drone swarm combat application, multi-source data fusion, and intelligent data analysis. E-mail: wanshichang@126.com

LI Hao was born in 1981. He received his B.E. degree in radar engineering, and M.E. degree in information and communication engineering from Air Force Radar Academy, Wuhan, China, in 2003 and 2006, respectively. He received his Ph.D. degree in electronic science and technology from Air Force Engineering University in 2017, Xi’an, China. His current research interests are swarm intelligence, drone swarm, intelligence systems and signal processing. E-mail: afeu_li@163.com

HU Yahui was born in 1980. She received her B.S. degree from Shanxi Normal University, Taiyuan, China, in 2003, M.S. degree from Air Force Early Warning Academy, Wuhan, China, in 2006 and Ph.D. degree from Wuhan University, Wuhan, China, in 2016. She is currently an associate professor at Air Force Early Warning Academy. Her research interests include air combat planning, aviation mission planning, big data and artificial intelligence. E-mail: hyh5800@163.com

WANG Xuhua was born in 1984. He received his B.E. degree in radar engineering, M.E. degree in computer application, and Ph.D. degree in electronic system integration engineering from Air Force Engineering University, Xi’an, China, in 2007, 2009 and 2013, respectively. He is currently an associate professor in Xidian University. His research interests include radar network modeling, drone swarm combat application, and anti- drone technology. E-mail: daleiwxh@163.com

CUI Siyuan was born in 2001. He received his B.E. degree in command information systems engineering from Air Force Early Warning Academy in Wuhan, China, in 2023. He is currently pursuing his M.S. degree in military command from Air Force Early Warning Academy. His research interests include drone bee colonies, intelligent systems, and signal processing. E-mail: zcehy07@163.com

Abstract

Abstract:

To address the issue of neglecting scenarios involving joint operations and collaborative drone swarm operations in air combat target intent recognition. This paper proposes a transfer learning-based intention prediction model for drone formation targets in air combat. This model recognizes the intentions of multiple aerial targets by extracting spatial features among the targets at each moment. Simulation results demonstrate that, compared to classical intention recognition models, the proposed model in this paper achieves higher accuracy in identifying the intentions of drone swarm targets in air combat scenarios.

Key words: drone, intention recognition, deep learning

Shichang WAN, Hao LI, Yahui HU, Xuhua WANG, Siyuan CUI. A multi target intention recognition model of drones based on transfer learning[J]. Journal of Systems Engineering and Electronics, 2025, 36(5): 1247-1258.

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References 33

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Feature	Statement	Instance
Azimuth angle/(°)	Azimuth angle from command center to direction of the aerial target	0° when facing north, divided into 360° clockwise for a full circle
Distance/km	Distance from the command center to the aerial target	150
Heading angle/(°)	Direction of flight of the aerial target	0° when facing north, divided into 360° clockwise for a full circle
RCS/m²	Size of the target’s radar echo	0≤RCS<2: small targets; 2≤RCS<4: medium targets; RCS≥4: large targets
Horizontal speed/(m/s)	Speed of the aerial target on the horizontal plane	200
Latitude/(°)	Angle between the normal of a point on the ellipsoid and the line plane of the equatorial plane	15.6
Longitude/(°)	Dihedral angle formed by the meridian plane passing through a certain place and the prime meridian plane	114.5

Intention name	Label	Variable symbol
Attack	0	gt₁
Monitoring	1	gt₂
Early warning	2	gt₃
Deception	3	gt₄
Interfere	4	gt₅

Hardware/Software	Version
Operating system	Windows 10
Computer memory	32 GB
Central processing unit	i7-9700 CPU
Language	Python 3.8.0
Compilation environment	Anaconda 3
Deep learning network	TensorFlow 2.0.0, keras2.8.0, etc.
Library	Os, pandas, matplotlib, etc.

Parameter	Value
Learning rate	0.01
Batch	16, 32, 64
Epoch	15, 20, 30···
Iteration	50, 100, 200, 300
Activation	Softmax

Class	Precision				Recall				F1-score
Class	1	2	3	4	1	2	3	4	1	2	3	4
Attack	90.4	89.3	89.0	91.3	87.3	86.1	87.0	88.6	88.82	87.67	87.99	89.10
Monitoring	88.3	88.3	88.5	90.3	89.3	88.3	88.5	90.2	88.80	88.30	88.50	89.39
Early warning	89.4	88.4	89.0	90.0	92.6	89.3	89.8	92.0	90.97	88.85	89.40	89.90
Deception	84.3	87.2	88.3	89.5	84.3	84.6	85.7	83.0	84.30	85.88	86.98	87.56
Interference	87.2	87.5	88.7	88.9	88.3	88.9	89.5	88.6	87.75	88.19	89.10	89.20

A multi target intention recognition model of drones based on transfer learning

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