A Fuzzy Logic Approach for Force Aggregation and Classification in Situation Assessment

Force Aggregation and Classification is a key component of situation assessment in the military domain. In this paper, a novel approach is proposed for force aggregation and classification using fuzzy belief networks. Fuzzy belief networks is a simple and fast method of inference from nodal observations that utilize bidirectional fuzzy influences that are propagated via fuzzy set membership functions. The fuzzy belief networks is utilized to represent the composition and structure of various types of groups. And the fuzzy logic inference is employed to infer the type of group with its attributes. In this paper, the nearest-neighbor clustering algorithm is utilized to merge targets into groups by position. Based on the aggregation result, the type of merged group is recognized via fuzzy belief networks. Finally, a simple application of the approach is described. The results show that the approach is available.