SUAS Flight Path Visualization

Small, uncrewed aircraft systems, i.e. drones, can provide small military units with valuable intelligence, surveillance and reconnaissance information. However, the flight paths of these drones must take into consideration a number of factors in order to successfully gather the needed information while minimizing the chances of being detected. These factors may include models of the drone’s noise and visibility signatures, models of human vision and auditory perception, environmental factors, and models of drone sensors, among others.

The goals of this project are to design, implement, and test a 3D visualization architecture that allows operators in small military units to plan, visualize, and revise drone flight paths over a specific terrain in order to fulfill the mission success criteria of effectively utilizing sensors over an area while minimizing detection. This visualization will provide extensibility with a plug-in or similar architecture, allowing future developers to easily add or revise drone noise and visibility models, human perceptual models, and sensor models.

Student Team
  • Alex Alcazar
  • Franciso Brito
  • Helen Dam
  • Alex Gaeta
  • Alberto Gonzalez
  • Sergio Maradiaga Olivera
  • Thad Owens
  • Mychal Salgado
  • Kevin Tang
  • Sergio Valadez Polanco
Project Sponsor
Army Research Lab
Project Liaisons
  • Paul Fedele
  • Eric Holder
Faculty Advisors
  • David Krum