Incorporation of indirect evidence into an evidence accrual techniquefor higher level data fusion

摘要

In many fusion problems, such as Level 2 (situational assessment) or Level 3 (impact assessment), observations frequently provide indirect, rather than direct, evidence. In such cases, the measurements affect the evidence level of interest through a functional relationship, such as speed being measured through the functional relationship between it and position observations over time. A general evidence accrual system that incorporates indirect observations into the evidence generation is developed. The technique, based on the concepts of first-order and reduced-order observer theory, can incorporate both observation quality and level of doctrine understanding in the uncertainty measure of the evidence. The technique does use a network structure with links and propagation of evidence, but, unlike a Bayesian taxonomy, it does not rely upon the strict probabilistic underpinnings. In this work, to demonstrate its proof of capability, the technique is applied to a force-on-force Level 2 fusion problem. The technique, based upon a Level 1 fusion target classification evidence accrual algorithm, uses a fuzzy Kalman filter to inject new evidence into the nodes of interest to modify the level of evidence. The fuzzy Kalman allows for the level of evidence to incorporate an uncertainty or quality measure into the report.