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Key Research Outcomes and Progress Update

Key Research Outcomes

Outcome
 Details
Class of cooperative localization algorithms
  • Using various sensing modalities (distance, AOA, TDOA, hybrid)
  • Perform significantly better than known alternatives, more robust and guaranteeing better precision
  • Some for precise localization of targets on (near-) spherical surfaces, with verification analysis using real-life trial data.

Identification of sensing/control architecture design principles
  • To ensure preservation of the shape of a formation.
  • Decentralized implementation/use is an issue
Set of formation control algorithms
  • To maintain the shape of a three vehicle formation in the presence of wind, constraints on airspeed, turning rate, etc
  • Desired shape is important in maximizing accuracy of cooperative localization with sensors on these vehicles
  • Obstacle avoidance
Robust self-localization algorithms for three (or more) UAV formations
  • A particular one based on inter-agent distance and bearing from two land marks information
  • Self-localization and tracking algorithms for UAV formations in loss of GPS measurement
Identification of sensor-target geometric principles  in  cooperative localization
  • For measuring/detecting/avoiding (near-) collinearity problems.
  • For sensor positioning to optimize quality of target localization.

 

 

Progress update

Setting up a demonstration to test any new algorithms using unmanned aerial vehicles (UAVs) would be a very expensive task. Before reaching this level of demonstration, the new algorithm is run in a hardware simulator, which is expected to happen by the end of 2008. If the DSTO is happy with the result, further UAV experiments can be arranged.