Intelligent UAV Swarm Cooperation for Multiple Targets Tracking

Longyu Zhou, Supeng Leng, Qiang Liu, Qing Wang

Research output: Contribution to journalArticleScientificpeer-review

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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
Number of pages12
JournalIEEE Internet of Things Journal
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Computational modeling
  • mobile target tracking
  • prediction
  • Prediction algorithms
  • scheduling.
  • Sensors
  • Target tracking
  • Task analysis
  • Trajectory
  • UAV swarm intelligence
  • Unmanned aerial vehicles

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