Using a blackboard architecture or expert system to identify obfuscated targets from symptoms

Abstract

A variety of techniques exist for enhancing or inferring the existence and characteristics of an obscured or partially concealed target. Targets, however, may be completely blocked from view, presenting nothing to enhance and no image area to extend inferentially. Despite the difficulty, concealed (particularly intentionally) targets may be the most important to detect. This paper proposes a technique for using a Blackboard Architecture or Expert system to infer a target's existence from symptoms (maneuvers of other units, water and soil deformation, etc.) and discusses the differences between the two approaches (Blackboard Architecture and expert system) for doing so.