Finding Subgroups of UAV Swarms Using a Trajectory Clustering Method

Unmanned Aerial Vehicles(UAV) swarm is a rapidly developing field, and with it comes the need to identify the swarm based on observations. The problem of trajectory clustering is put forward in the identification of UAV swarms, especially modularized UAV swarms. We propose a new method of Network In...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-01, Vol.1757 (1), p.12131
Main Authors: Gu, Kongjing, Mao, Ziyang, Qi, Mingze, Duan, Xiaojun
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Fault tolerance
Multiagent systems
Multidimensional data
Physics
Subgroups
Trajectory clustering
UAV swarm
Unmanned aerial vehicles
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Summary:Unmanned Aerial Vehicles(UAV) swarm is a rapidly developing field, and with it comes the need to identify the swarm based on observations. The problem of trajectory clustering is put forward in the identification of UAV swarms, especially modularized UAV swarms. We propose a new method of Network Integrated trajectory clustering(NIT) to solve the trajectory clustering problem in a fast-changing and chaotic environment which requires a quick response, fault tolerance, and accuracy. The experiment results prove the flexibility and adaptability of the NIT method towards various demands and multi-dimensional data. Moreover, the algorithm proposed based on the method shows priority over the other three trajectory clustering methods(DTW, Fréchet distance, GMM) on the accuracy, and fault tolerance in clustering swarm trajectories. The method raised in this paper is an innovation to both multi-agent systems identification and trajectory clustering methods.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1757/1/012131