|
With the advent of low Size,
Weight, Power, and Cost (SWaP-C)
sensors, an option is to use small UAVs launched into obscured, confounded,
or otherwise denied areas into which they can maneuver around obscurations
and close near to targets to discriminate them from confounders. The
envisioned CONcept of Operations (CONOPS) for
these types of missions is shown in the diagram above in which there are
four UAVs approaching from the West.
The targets to the East have been designated in the Joint Forces Air
Component Commander’s (JFACC) Air Tasking Order (ATO). The targets have 95% Areas of Uncertainty
(AOU) shown as ellipses on the display. There is high confidence the
targets are in the area but the Position, Identification, Classification,
and Composition (PICC) is not good enough for a fire-and-forget weapons
solution satisfying Joint Task Force (JTF) commander’s Rules of Engagement
(RoE).
Thus, the UAVs Collaborate to Acquire the Target
PICC that meets mission parameters.
For a variety of mission reasons as well as the low SWaP-C employment, inter-UAV communications is limited
and reachback to Command and Control (C2) or
Intelligence, Surveillance, and Reconnaissance (ISR) assets is prohibitive.
Hence, the UAVs effectively are operating in an Autonomous manner.
Therefore, this mission space can be called “Collaborating – Autonomous
Target Acquisition (C-ATA)”.
A Data Fusion
solution to this mission requirement consists of an architecture,
algorithms, and ontologies. At their core, Data Fusion (DF) processes are
information-based estimation processes, directed at providing the
best possible mission-critical knowledge to support various mission tasks
and functions. DF functions in each C-ATA UAV estimate PICC using
multi-modal, multi-sensor, and multi-platform data. Each C-ATA UAV has a
different sensor aspect angle and range to the targets which changes as the
formation progresses along the search path so that “new” sensor data is
constantly arriving for DF processing. Each C-ATA UAV’s DF processes
formulate and update object hypotheses and broadcast them to the other
C-ATA UAVs as PICC reports. The reports are compact but with high data
fidelity suitable for correlation and fusion with objects in each of the
C-ATA UAV’s local fusion files. Because the fusion algorithms and prelaunch
loaded a priori data in each C-ATA UAV are identical and they share
all their object detections and updates, their fusion files are nearly
identical, one aspect of implicit collaboration.
|
