An autonomous fuzzy logic architecture for multisensor data fusion

Fuzzy logic techniques have become popular to address various processes for multisensor data fusion. Examples include: (1) fuzzy membership functions for data association, (2) evaluation of alternative hypotheses in multiple hypothesis trackers, (3) fuzzy-logic-based pattern recognition (e.g., for feature-based object identification), and (4) fuzzy inference schemes for sensor resource allocation. These approaches have been individually successful but are limited to only a single subprocess within a data fusion system. At The Pennsylvania State University, Applied Research Laboratory, a general-purpose fuzzy logic architecture has been developed that provides for control of sensing resources, fusion of data for tracking, automatic object recognition, control of system resources and elements, and automated situation assessment. This general architecture has been applied to implement an autonomous vehicle capable of self-direction, obstacle avoidance, and mission completion. The fuzzy logic architecture provides interpretation and fusion of multisensor data (i.e., perception) as well as logic for process control (action). This paper provides an overview of the fuzzy logic architecture and a discussion of its application to data fusion in the context of the Department of Defense (DoD) Joint Directors of Laboratories (JDL) Data Fusion Process Model. A new, robust, fuzzy calculus is introduced. An example is provided by modeling a component of the perception processing of a bat