Data Fusion Architectures for Sensor Platforms

Abstract

The role of data fusion in sensor platforms is becoming increasingly important in various domains of science, technology and business. Fusion pertains to the merging or integration of information towards an enhanced level of awareness. Multi-sensor fusion can be applied to any system that must retrieve and synthesize data from numerous sources. Its applications can be found in a variety of technology settings, from robotics, structural and vehicle health monitoring to communications, space science and telemedicine. This work is a canonical overview of several major fusion architectures developed from the remote sensing and defense community. We present several types of architecture for managing multi-sensor data fusion, specifically as they relate to the tracking-correlation function and blackboard processing representations in knowledge engineering. Object-Process Methods are used to model the information fusion process and supporting subsystems. Finally, we discuss the importance of fusion to the concept and operation of the Semantic Web. Semantic networks offer powerful ways to exploit the synergy of multi-sensor data platforms. Their operation is based on synthesis of fusion with ontology models for knowledge representation. We discuss the importance of fusion as a reuse process in ontological engineering, and review key lifecycle models in ontology development. The evolutionary approach to ontology development is considered the most useful and adaptable to the complexities of semantic networks. Several potential applications for data fusion are screened and ranked according to the Joint Directors of Laboratories (JDL) process model for information fusion. Based on these predetermined criteria, the case of medical diagnostic imaging was found to offer the most promising applications for fusion, on which future product platforms can be built.