Proc. SPIE. 7709, Cyber Security, Situation Management, and Impact Assessment II; and Visual Analytics for Homeland Defense and Security II
KEYWORDS: Satellites, Receivers, Telecommunications, Satellite communications, Chemical elements, Data communications, Information security, Computer security, Global Positioning System, Situational awareness sensors
Situation Awareness (SA) is the perception of environmental elements within a volume of time and space, the
comprehension of their meaning, and the projection of their future status. In a military environment the most critical
elements to be tracked are followed elements are either friendly or hostile forces. Poor knowledge of locations of
friendly forces easily leads into the situation in which the troops could be under firing by own troops or in which
decisions in a command and control system are based on incorrect tracking. Thus the Friendly Force Tracking (FFT) is a
vital part of building situation awareness.
FFT is basically quite simple in theory; collected tracks are shared through the networks to all troops. In real world, the
situation is not so clear. Poor communication capabilities, lack of continuous connectivity n and large number of user on
different level provide high requirements for FFT systems.
In this paper a simple architecture for Friendly Force Tracking is presented. The architecture is based on NFFI (NATO
Friendly Force Information) hubs which have two key features; an ability to forward tracking information and an ability
to convert information into the desired format. The hub based approach provides a lightweight and scalable solution,
which is able to use several types of communication media (GSM, tactical radios, TETRA etc.). The system is also
simple to configure and maintain. One main benefit of the proposed architecture is that it is independent on a message
format. It communicates using NFFI messages, but national formats are also allowed.
Situational awareness is critical on the modern battlefield. A large amount of intelligence information is collected to
improve decision-making processes, but in many cases this huge information load is even decelerating analysis and
decision-making because of the lack of reasonable tools and methods to process information. To improve the decision
making process and situational awareness, lots of research is done to analyze and visualize intelligence information data
automatically. Different data fusion and mining techniques are applied to produce an understandable situational picture.
Automated processes are based on a data model which is used in information exchange between war operators. The data
model requires formal message structures which makes information processing simpler in many cases. In this paper,
generated formal intelligence message data is visualized and analyzed by using the self-organizing map (SOM). The
SOM is a widely used neural network model, and it has shown its effectiveness in representing multi-dimensional data in
two or three dimensional space. The results show that multidimensional intelligence data can be visualized and classified
with this technique. The SOM can be used for monitoring intelligence message data (e.g. in purpose of error hunting),
message classification and hunting correlations. Thus with the SOM it is possible to speed up the intelligence process
and make better and faster decisions.