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In this paper, we present our initial findings demonstrating a cost-effective approach to Aided Target Recognition (ATR)
employing a swarm of inexpensive Unmanned Aerial Vehicles (UAVs). We call our approach Distributed ATR (DATR).
Our paper describes the utility of DATR for autonomous UAV operations, provides an overview of our methods, and the
results of our initial simulation-based implementation and feasibility study. Our technology is aimed towards small and
micro UAVs where platform restrictions allow only a modest quality camera and limited on-board computational
capabilities. It is understood that an inexpensive sensor coupled with limited processing capability would be challenged
in deriving a high probability of detection (Pd) while maintaining a low probability of false alarms (Pfa). Our hypothesis
is that an evidential reasoning approach to fusing the observations of multiple UAVs observing approximately the same
scene can raise the Pd and lower the Pfa sufficiently in order to provide a cost-effective ATR capability. This capability
can lead to practical implementations of autonomous, coordinated, multi-UAV operations.
In our system, the live video feed from a UAV is processed by a lightweight real-time ATR algorithm. This algorithm
provides a set of possible classifications for each detected object over a possibility space defined by a set of exemplars.
The classifications for each frame within a short observation interval (a few seconds) are used to generate a belief
statement. Our system considers how many frames in the observation interval support each potential classification. A
definable function transforms the observational data into a belief value. The belief value, or opinion, represents the
UAV's belief that an object of the particular class exists in the area covered during the observation interval. The opinion
is submitted as evidence in an evidential reasoning system. Opinions from observations over the same spatial area will
have similar index values in the evidence cache. The evidential reasoning system combines observations of similar
spatial indexes, discounting older observations based upon a parameterized information aging function. We employ
Subjective Logic operations in the discounting and combination of opinions. The result is the consensus opinion from all
observations that an object of a given class exists in a given region.
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