The success of unmanned aerial vehicles (UAV) in the Iraq and Afghanistan
conflicts has led to increased interest in further digitalization of the United States armed
forces. Although unmanned systems have been a tool of the military for several
decades, only recently have advances in the field of Micro-Electro-Mechanical Systems
(MEMS) technology made it possible to develop systems capable of being transported
by an individual soldier. These miniature unmanned systems, more commonly referred
to as micro air vehicles (MAV), are envisioned by the Department of Defense as being
an integral part of maintaining America?s military superiority.
As researchers continue to make advances in the miniaturization of flight
hardware, a new problem with regard to MAV field operations is beginning to present
itself. To date, little work has been done to determine an effective means of collecting,
analyzing, and handling information that can satisfy the goal of using MAVs as tools for
persistent surveillance. Current systems, which focus on the transmission of analog
video streams, have been very successful on larger UAVs such as the RQ-11 Raven but
have proven to be very demanding of the operator. By implementing a new and innovative data processing methodology, currently existing hardware can be adapted to
effectively present critical information with minimal user input.
Research currently being performed at Texas A&M University in the areas of
attitude determination and image processing has yielded a new application of
photographic projection. By replacing analog video with spatially aware high-resolution
images, the present MAV handheld ground control stations (GCS) can be enhanced to
reduce the number of functional manpower positions required during operation.
Photographs captured by an MAV can be displayed above pre-existing satellite imagery
to give an operator a lasting reference to the location of objects in his vicinity. This
newly generated model also increases the functionality of micro air vehicles by allowing
for target tracking and energy efficient perch and stare capabilities, both essential
elements of persistent surveillance.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2009-08-7052 |
| Date | 2009 August 1900 |
| Creators | Goodnight, Ryan David |
| Contributors | Reed, Helen L. |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | application/pdf |
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