Intelligent UAV Swarm Cooperation for Multiple Targets Tracking

Longyu Zhou, Supeng Leng, Qiang Liu, Qing Wang

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
13 Downloads (Pure)

Abstract

With the advantages of easy deployment and flexible usage, Unmanned Aerial Vehicle (UAV) has advanced the Multi-Target Tracking (MTT) applications. The UAV-MTT system has great potentials to execute dull, dangerous, and critical missions for frontier defense and security. A key challenge in UAV-MTT is how to coordinate multiple UAVs to track diverse invading targets accurately and consecutively. In this paper, we propose a UAV swarm-based cooperative tracking architecture to systematically improve the UAV tracking performance. We design an intelligent UAV swarm-based cooperative algorithm for consecutive target tracking and physical collision avoidance. Moreover, we design an efficient cooperative algorithm to predict the trajectory of invading targets accurately. Our simulation results demonstrate that the swarm behaviors stay stable in realistic scenarios with perturbing obstacles. Compared with state-of-the-art solutions such as the matched deep Q-network, our algorithms can increase tracking accuracy by 60%, reduce tracking delay by 23%, and achieve physical collision-avoidance during the tracking process.

Original languageEnglish
Pages (from-to)743-754
Number of pages12
JournalIEEE Internet of Things Journal
Volume9
Issue number1
DOIs
Publication statusPublished - 2021

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Mobile target tracking
  • prediction
  • scheduling
  • unmanned-aerial-vehicle (UAV) swarm intelligence (SI)

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