A new method for representing linguistic quantifications by random sets with applications to tracking and data fusion

There is an obvious need to be able to integrate both linguistic-based and stochastic-based input information in data fusion. In particular, this need is critical in addressing problems of track association, including cyber-state intrusions. This paper treats this issue through a new insight into how three apparently distinct mathematical tools can be combined: "boolean relational event algebra" (BREA), "one point random set coverage representations of fuzzy sets" (OPRSC), and "complexity-reducing algorithm for near optimal fusion" (CRANOF).