|
Home
|
|
DF Level 0
Level
0 functions operate on sensor data to produce (extract) features using
inference models. For the imaging sensors, Object
classification (labeling) is inferable using inference models developed by
Deep Learning (DL) processes trained in the mission planning phase on
targets and environments representative of those expected in the ATO area.
The Level 0 outputs are Object and Event detections such as radar
“contacts” (reflected and received power exceeds some Constant False Alarm
Rate (CFAR) threshold) and image bounding boxes about some object type of
interest. For Electro-Magnetic (EM) sensors, the C-ATA UAV can come near to
the targets to detect and triangulate intentional and non-intentional EM
emissions from, e.g., Low Probability of Intercept (LPI) emissions, site
motor generators, and computer networks. In this modality, the Level 0
outputs are estimated waveform parameters like carrier frequency and
modulation along with Lines of Bearing (LOB). DF Level 0 often includes
knowledge bases of a priori data drawn from an ontology resulting from
an Intelligence Preparation of the Operational Environment (IPOE) including target and
confounder Characteristics and Performance (C&P), other Scientific and
Technical Intelligence (S&TI), Order-of-Battle (OB), terrain data,
ambient objects, man-made structures, avoidance areas, and other GEOspatial INTelligence
(GEOINT). This data is structured into a formal ontology to support
processing by DF functions.
|
|
DF Level 1
Level
1 processes estimate properties of Events and Objects, where Position, Identification,
Classification, and Composition properties (PICC) are developed from Level 0’s extracted
features using, typically, statistical estimation (e.g., Kalman filter) and
probabilistic
inference (e.g., Bayesian, Dempster-Shafer, fuzzy)
algorithms.
As
part of Level 1 fusion, Classification processes estimate that an object is
a type of a class, e.g., SA-2 missile launcher or Spoon Rest acquisition
radar. Classification is about “what”. Identification says the object is a member-of or belongs-to an Organization, e.g., NATO. Identification is about “who”,
involving estimation of attribution/ownership. Organization here is in a
general sense and can include not just nationalities but also alliances or
subdivisions or groupings thereof.
|
|
DF Level 2
Level
2 is concerned about linkages between Objects and Events. When the linkages are about wholes and
their parts, it is Composition. An example is the Composition of an SA-2
SAM site consisting of parts,
such as missile launchers, radars, generators, command and control
trailers, arranged in some standard layouts. This can be represented as a
graph where the nodes are the Objects and Events and the edges are the type
of relationships between them, e.g., whole-part, distance-from. Similarly,
the a priori data, structured into a formal ontology, can be formed
into graphs. Directed Attributed Relational Graphs (DARG) and associated
graph association methods (see, e.g., ) have high
potential for this type of Level 2 DF. The DARG analytical processes involve
representation of layered data, cross-layer (graph) association, and
associated evidence-to-situation information by graph-matching. In recent
work, an inexact subgraph matching algorithm was developed as a variation
of the subgraph isomorphism approach for situation assessment.
|
|
DF Level 3
Level 3 is concerned with prediction. In this example, this means inferring the
Tactics, Techniques, and Procedures (TTP) being used by the adversary and
then the adversary’s possible and probable Courses of Action (CoA). In this C-ATA example it could be the air
defense TTP and the Position of Intended Movement (PIM) of the targets.
|
|
DF Level 4
Level
4 is concerned with Information-optimizing
resource management. Each C-ATA UAV’s DF estimation
process is coupled with a sensor/source management process. An integrated
approach to sensor management is based on an embedded scheme for trading
off mission objectives with optimal sensor data acquisition and is
integrated into the real-time DF processes.
|
|
Gross, G.,
Nagi, R. and Sambhoos, K. "Continuous
Preservation of Situational Awareness through Incremental/Stochastic
Graphical Methods," 14th International Conference on Information
Fusion, Chicago, IL, 26-29 July 2011.
|
|
