Situation assessment: Uncertainty representation in inexact graph matching

In conventional warfare as well as counter-insurgency (COIN) operations, the understanding of the situation is extremely vital to assure a sense of security. Intelligence in COIN is about people, and deployed units in the field are the best sources of intelligence. The basic task of analysts working in current operations is to analyze past and current enemy actions and to look for changes in the insurgents? approach or tactics. To do this, graphical methods have proven to be effective. In recent work (Sambhoos et al., 2009) have developed an inexact subgraph matching algorithm as a variation of the subgraph isomorphism approach for situation assessment in higher level data fusion. In this paper we enhance this procedure to represent inaccurate observations or data estimates, and inaccurate structural representations of a state of interest, thus accounting for the uncertainties. We have reviewed various probabilistic and possibilistic uncertainty representations and transformations between them. This comprehensible approach will give pragmatic estimates providing rigor and sound understanding during situation assessment.