New Method for Representing Linguistic Quantifications by Random Sets with Applications to Tracking and Data Fusion

摘要

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).