Rethinking the JDL Data Fusion Levels

Abstract

Recent work has occasioned a rethinking of the well-known JDL Data Fusion levels. The suggested revised partitioning of data fusion functions is designed to capture the significant differences in the types of input data, models, outputs, and inferencing appropriate to broad classes of data fusion problems. In general, the recommended partitioning is based on different aspects of a situation for which the characterization is of interest to a system user. In particular, a given entity − whether a signal, physical object, aggregate or structure − can often be viewed either (a) as an individual whose attributes, characteristics and behaviors are of interest or (b) as an assemblage of components whose interrelations are of interest. From the former point of view, discrete physical objects (the “targets ” of level 1 data fusion) are components of a situation. From the latter point of view, the targets are themselves situations; i.e. contexts for the analysis of components and their relationships. A complementary set of resource management levels − duals of the fusion levels − is also introduced. Among other things, these encompass such fusion-related functions as (a) management of the data collection, signal and data processing, and fusion processes and (b) management of models used in data fusion processes; to include building and refining sensor and system performance models, bias models for calibration or registration, models of target characteristics, and models of the characteristics of situations. 1