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Boosting intelligence analysis process and situation awareness using the self-organizing map

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.