Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (10): 3416-3422.doi: 10.13229/j.cnki.jdxbgxb.20240040

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Trajectory prediction and interception algorithm for large maneuvering multi-rotor UAV

Ming-hui SUN1(),Jing-yuan BIAN2,Jia-xing CHE3,Zhen-jie SHU4   

  1. 1.College of Computer Science and Technology，Jilin University，Changchun 130012，China
    2.Unilink Intelligent Control（Beijing） Technology Co. ，Ltd. ，Beijing 101318，China
    3.School of Automation Science and Electrical Engineering，Beihang University，Beijing 100191，China
    4.China Aero?Polytechnology Establishment of AVIC，Beijing 100073，China
  • Received:2024-01-12 Online:2025-10-01 Published:2026-02-03

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Abstract:

Aiming at the security risk of the unauthorized rejected UAV in the restricted flying area， and the problem that it is difficult to counter， this paper proposes an algorithm to use a UAV to intercept the unlicensed and uninformed UAV at high speed. Our algorithm uses a navigation and control architecture to solve this problem. In the aspect of navigation， low-cost omnidirectional perception was realized through the combination of camera and laser radar. Through the fusion of high precision and low frequency lidar data and high frequency and low precision vision data， accurate target position estimation and tracking are obtained. In the control aspect， the dynamic authority allocation control method based on trajectory prediction is used to realize the effective countermeasures against the large maneuvering UAV. Finally， the effectiveness of the proposed algorithm is verified by experiments and simulations. It is verified that the proposed algorithm can lock the denied UAV with a success rate of more than 90% and intercept the UAV with a success rate of more than 85%.

Key words: intelligent control and robotics, trajectory prediction, fusion perception, position estimation

CLC Number: 

  • TP391.8

Fig.1

Flow chart of lidar perception"

Table 1

Parameter of sensor"

SensorUpdate rateMeasurements
Camera30 HzPosition
Lidar10 HzPosition

Fig.2

Fused position estimation model"

Fig.3

Target motion trajectory"

Fig.4

Experiment frame and experiment interceptor UAV"

Fig.5

Target position computed by camera and estimated position"

Fig.6

Improvement of estimation accuracy by radar data"

Fig.7

Target lock-on"

Fig.8

Predicted point"

Fig.9

Trajectory of intercept"

Table 2

Comparison with existing methods"

拦截方式

拦截速度

/（m·s-1）

感知角度

/（°）

锁定成功率

/%

视觉伺服［3］212075.23

光电吊舱+

PN制导律［4，13］

<8120—

融合感知+

轨迹预测拦截

>1236099.4

Table 3

Success rate of simulation and experiment"

项目一次拦截成功率/%多次拦截成功率/%
仿真100100
实飞>85100
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