Decentralised ground target tracking with heterogeneous sensing nodes on multiple UAVs

  • Authors:

  • Matthew Ridley;Eric Nettleton;Ali Göktoǧan;Graham Brooker;Salah Sukkarieh;Hugh F. Durrant-Whyte

  • Affiliations:

  • School of Aerospace, Mechanical and Mechatronic Engineering, Australian Centre for Field Robotics, The University of Sydney, NSW, Australia;School of Aerospace, Mechanical and Mechatronic Engineering, Australian Centre for Field Robotics, The University of Sydney, NSW, Australia;School of Aerospace, Mechanical and Mechatronic Engineering, Australian Centre for Field Robotics, The University of Sydney, NSW, Australia;School of Aerospace, Mechanical and Mechatronic Engineering, Australian Centre for Field Robotics, The University of Sydney, NSW, Australia;School of Aerospace, Mechanical and Mechatronic Engineering, Australian Centre for Field Robotics, The University of Sydney, NSW, Australia;School of Aerospace, Mechanical and Mechatronic Engineering, Australian Centre for Field Robotics, The University of Sydney, NSW, Australia

  • Venue:
  • IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
  • Year:
  • 2003

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Abstract

This paper presents real time results of a decentralised air-borne data fusion system tracking multiple ground based targets. These target estimates are then used to construct a map of the environment. A decentralised communication strategy is employed which is robust to communication latencies and dropouts and results in each sensing node having a local estimate using global information. In addition, this paper describes both hardware and algorithms used to deploy two sensor nodes for such a task. Two sensor types will be discussed, vision and mm wave radar. The problems introduced by locating the sensors on air vehicles are both interesting and challenging. A total of four unmanned air vehicles will be employed to carry node payloads. Weight and power restrictions of the payloads coupled with the vehicle dynamics make the task of processing and fusing vision and radar based data a challenging problem indeed. This paper aims to highlight many of the problems that have been encountered in developing both hardware and software to operate under such constraints.