Information Fusion for Onboard and Offboard Avionics

The objective of this research is to define and validate an Avionics Information Fusion System (AIFS) which optimally combines onboard and offboard data. We propose an architecture containing an offboard data processor to extract data relevant to the mission, and a set of fusion modules for recursively associating sensor reports, tracking targets, and classifying targets. These modules are derived from a well-posed mathematical formulation which enables us to define precise interfaces among the fusion modules. This approach provides three benefits to Air Force Wright Labs. (AFWL). First, it enables AFWL to construct a fusion algorithm with close-to-optimal target tracking and classification performance. Second, it allows AFWL to study new fusion algorithms by implementing alternate algorithms for each module. Third, it allows AFWL to process data from any combination of sensors making the architecture applicable to a variety of fighter platforms and missions. We will show that the proposed AIFS can increase a pilot's situational awareness by providing him with a clearer battlefield picture consistent with mission objectives. We will define performance metrics that reflect mission objectives as well as evaluate algorithmic performance. We will validate our system using a simple but realistic air-to-ground scenario simulation.