Date of Award

3-24-2016

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Electrical and Computer Engineering

First Advisor

Brian G Woolley, PhD.

Abstract

Aerial refueling is essential to the United States Air Force (USAF) core mission of rapid global mobility. However, in-flight refueling is not available to remotely piloted aircraft (RPA) or unmanned aerial systems (UAS). As reliance on drones for intelligence, surveillance, and reconnaissance (ISR) and other USAF core missions grows, the ability to automate aerial refueling for such systems becomes increasingly critical. New refueling platforms include sensors that could be used to estimate the relative position of an approaching aircraft. Relative position estimation is a key component to solving the automated aerial refueling (AAR) problem. Analysis of data from a one-seventh scale, real world refueling scenario demonstrates that the relative position of an approaching aircraft can be estimated at rates between 10 Hz and 30 Hz using stereo vision. Linear regression models on position estimate accuracies predict results reported by other research in the simulation domain, suggesting that real world accuracies are comparable to simulation domain accuracies reported by others. Further, by seeding the position estimation algorithm with previous position estimates, subsequent errors in position estimation are reduced

AFIT Designator

AFIT-ENG-MS-16-M-252

DTIC Accession Number

Pending

Share

COinS