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CLC number: TP391

On-line Access: 2021-10-08

Received: 2020-07-20

Revision Accepted: 2021-03-31

Crosschecked: 2021-08-16

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Zhi Zheng


Shuncheng Cai


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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.10 P.1334-1350


A collaborative target tracking algorithm for multiple UAVs with inferior tracking capabilities

Author(s):  Zhi Zheng, Shuncheng Cai

Affiliation(s):  College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, China

Corresponding email(s):   zhengz@fjnu.edu.cn

Key Words:  Collaborative target tracking, Intent estimation, MDA-Voronoi diagram, Multi-UAV, Inferior tracking capability

Zhi Zheng, Shuncheng Cai. A collaborative target tracking algorithm for multiple UAVs with inferior tracking capabilities[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(10): 1334-1350.

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T1 - A collaborative target tracking algorithm for multiple UAVs with inferior tracking capabilities
A1 - Zhi Zheng
A1 - Shuncheng Cai
J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2000362

Target tracking is one of the hottest topics in the field of drone research. In this paper, we study the multiple unmanned aerial vehicles (multi-UAV) collaborative target tracking problem. We propose a novel tracking method based on intention estimation and effective cooperation for UAVs with inferior tracking capabilities to track the targets that may have agile, uncertain, and intelligent motion. For three classic target motion modes, we first design a novel trajectory feature extraction method with the least dimension and maximum coverage constraints, and propose an intention estimation mechanism based on the environment and target trajectory features. We propose a novel Voronoi diagram, called MDA-Voronoi, which divides the area with obstacles according to the minimum reachable distance and the minimum steering angle of each UAV. In each MDA-Voronoi region, the maximum reachable region of each UAV is defined, the upper and lower bounds of the trajectory coverage probability are analyzed, and the tracking strategies of the UAVs are designed to effectively reduce the tracking gaps to improve the target sensing time. Then, we use the Nash Q-learning method to design the UAVs’ collaborative tracking strategy, considering factors such as collision avoidance, maneuvering constraints, tracking cost, sensing performance, and path overlap. By designing the reward mechanism, the optimal action strategies are obtained as the control input of the UAVs. Finally, simulation analyses are provided to validate our method, and the results demonstrate that the algorithm can improve the collaborative target tracking performance for multiple UAVs with inferior tracking capabilities.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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