Advanced Feature Aided Track and CDI Fusion Processing of Data from...

Successful missile defense operations require an accurate real-time single integrated missile picture, while minimizing data throughput and bandwidth requirements. Current fusion approaches treat kinematic and feature fusion as separate processes and have higher rates of correlation errors and target typing errors. Our integrated fusion process reduces these errors by jointly considering both kinematic and feature data. Specifically, this feature aided approach uses data such as RCS, length, and radar signal modulation (RSM) and other features to decrease the probability of incorrect association, improve CDI fusion, reduce false track likelihood, and enhance track accuracy. This approach improves overall system effectiveness by increasing the ability to resolve high speed closely spaced objects, track maneuvering objects (boosting missiles and maneuvering/unstable reentry vehicles), and identify and destroy lethal objects within dense target clusters containing decoys and debris. Intelligent Systems Research designed, developed and tested a prototype proof-of-concept system. This prototype integrated track and CDI fusion process operates within a distributed component architecture that receives statistically independent kinematic and associated feature data from network sensors and simultaneously processes this data. Our approach operates on sensor reports containing feature and kinematic measurements, tracklets, and object type likelihoods.