Incorporation of Partially Observable Evidence Into an Evidence Accrual Data Fusion Technique

In many sensor fusion problems, such as level 1 (object refinement), 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 or level of interest through a functional relationship. Often, these observations can be considered partially observable, such as the relationship between a bearings-only measurement and target position. A general evidence accrual system that incorporates these partially- observable 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 into the uncertainty measure of the evidence. Unlike a Bayesian taxonomy, the proposed method does not rely upon the strict probabilistic underpinnings, but instead uses a network structure with links and propagation of evidence. In this work, proof of capability is demonstrated by applying the technique to a Level 1 classification fusion problem where the observations are target attributes. The technique, based upon an existing 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 filter allows for the level of evidence to incorporate an uncertainty or quality measure into the report.