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Towards Reliable Identification and Tracking of Drones Within a Swarm

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journal contribution
posted on 2024-07-01, 05:37 authored by N Kumari, Kevin LeeKevin Lee, Jan Carlo Barca, Chathu RanaweeraChathu Ranaweera
AbstractDrone swarms consist of multiple drones that can achieve tasks that individual drones can not, such as search and recovery or surveillance over a large area. A swarm’s internal structure typically consists of multiple drones operating autonomously. Reliable detection and tracking of swarms and individual drones allow a greater understanding of the behaviour and movement of a swarm. Increased understanding of drone behaviour allows better coordination, collision avoidance, and performance monitoring of individual drones in the swarm. The research presented in this paper proposes a deep learning-based approach for reliable detection and tracking of individual drones within a swarm using stereo-vision cameras in real time. The motivation behind this research is in the need to gain a deeper understanding of swarm dynamics, enabling improved coordination, collision avoidance, and performance monitoring of individual drones within a swarm. The proposed solution provides a precise tracking system and considers the highly dense and dynamic behaviour of drones. The approach is evaluated in both sparse and dense networks in a variety of configurations. The accuracy and efficiency of the proposed solution have been analysed by implementing a series of comparative experiments that demonstrate reasonable accuracy in detecting and tracking drones within a swarm.

History

Journal

Journal of Intelligent and Robotic Systems: Theory and Applications

Volume

110

Article number

84

Pagination

1-31

Location

Berlin, Germany

Open access

  • Yes

ISSN

0921-0296

eISSN

1573-0409

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Issue

2

Publisher

Springer