Disjunctive belief rule base spreading for threat level assessment with heterogeneous, insufficient, and missing information

Abstract

The threat level assessment of a suspect object is instrumental for developing combatant responses and effecting situational awareness. In practical conditions, it suffers from four challenges: (1) inputs are gathered from heterogeneous sources, which could also result in heterogeneous formats and types, (2) historic records are insufficient owing to limited number of suspect objects and under-developed detection technologies and devices, (3) missing information resulted from inadequacy of input gathering, exchanging, and storage, and (4) the assessment process should be accessible to experts and decision makers to provide reasonability. In this study, the belief rule base (BRB) under a disjunctive assumption (disjunctive BRB) is applied because it can handle heterogeneous and insufficient information as well as provide access to experts and decision makers. Furthermore, a new disjunctive BRB spreading approach with six steps is proposed to specifically handle the missing information challenge. First, multiple sub-BRBs are initialized and then optimized using heterogeneous and insufficient information. Sub-BRBs are in different belief structures, which are part of the complete belief structure. Second, a self-organizing map (SOM)-based procedure is proposed to integrate multiple sub-BRBs into one final BRB (f-BRB) in the complete belief structure with consideration of the weights of sub-BRBs. A practical threat level assessment case is studied to validate the efficiency of the proposed disjunctive BRB spreading approach. The case study results show that (1) optimization can help improve the modeling accuracy of sub-BRBs to different degrees while initial sub-BRBs have shown varied performances, and (2) f-BRB produces the highest modeling accuracy on both historic records and newly detected suspect objects.