Date of Award
Master of Science
Department of Electrical and Computer Engineering
Michael L. Talbert, PhD
Swarming Unmanned Aerial Vehicles (UAVs) are the future of Intelligence, Surveillance and Reconnaissance (ISR). Swarms of hundreds of these vehicles, each equipped with multiple sensors, will one day fill the skies over hostile areas. As the sensors collect hundreds of gigabytes of data, telemetry data links will be unable to transmit the complete data picture to the ground in real time. The collected data will be stored on board the UAVs and selectively downloaded through queries issued from analysts on the ground. Analysts expect to find relevant sensor data within the collection of acquired sensor data. This expectation is not a quantified value, rather a confidence that this relevant data exists. An expectation of the likely quality of the available sensor information is determined by the user through the use of the methods and tools developed in this thesis. This work develops swarm coverage analysis models using position in time data from the swarm. With these models, a geometric analysis of the swarm is conducted that shows analysts when and where the swarm likely collected sensor data most relevant to a need. Convex hulls are used to calculate areas of coverage as well as swarm and sensor densities. Target profiling algorithms are developed that show target coverage over time from the swarm for each sensor type. Target-centric and sensor-centric analyses allow analysts to quickly determine where individual swarm agents were relative to a target at any point during the mission. Finally a series of visualizations of the swarm and targets are created that allow the analyst to view swarm activity from the perspective of individual swarm members or targets.
DTIC Accession Number
Baldwin, Patrick D., "Modeling Information Quality Expectation in Unmanned Aerial Vehicle Swarm Sensor Databases" (2005). Theses and Dissertations. 3844.