Optimum ID Sensor Fusion for Multiple Target Types

摘要

Bayesian techniques have been employed to combine, or fuse, reports from multiple target identification (ID) sensors. Recent work has shown that a 'cost' criterion can be used to make optimum target declaration decisions from the Bayesian representations of the ID sensors. However, the 'cost' method, as previously formulated, only works when choosing between two types of target declarations (e.g., friend vs. hostile). This paper provides an alternative formulation to the cost methodology previously developed for Bayesian analysis of ID sensor fusion. This alternative formulation is then used to develop a formulation that works for multiple types of targets. Therefore, this alternative formulation will allow analysts and military commanders to include neutral targets in the ID fusion process. Indeed, the technique also enables optimum ID sensor fusion when considering multiple additional types of targets (e.g., coalition force, hostile fighters, hostile bombers, and hostile cruise missiles).