|
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.
|
