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Data Fusion and Registration (DataFusR) System

The successful DataFusR phase II SBIR project developed processes for fusing information from multiple, widely separated radars together with EO/IR sensor data to significantly enhance the accuracy and reliability of ballistic missile defense (BMD) systems over traditional single radar systems. DataFusR addressed the discrimination and tracking component of the BMD problem by autonomously fusing estimates of the properties for each perceived entity in the threat cloud environment. The DataFusR project demonstrated capabilities to discriminate targets from decoys and clutter, track the targets and display the results in a Single Integrated Air Picture (SIAP). A proposed Multi-Source Data Fusion (MSDF) system could provide intelligent control of DataFusR processes.

This effort both developed new processes and enhanced existing technology necessary to process, and fuse information from multiple radars (either at the same or different frequency) to form a single integrated air picture (SIAP) of the ground midcourse ballistic target environment. DataFusR demonstrated the technology in increasingly more realistic scenarios using simulated data from multiple radars and other multi-modality sensors. DataFusR operated on a combination of sensor measurements, features, track states, and object types to produce a highly accurate SIAP.

DataFusR simulations incorporated theoretical/mathematical target and clutter models derived from the physics of radar. An extensive set of simulation scenarios were generated including single and multiple targets with and without clutter. Both 2D and 3D simulations were performed. The simulations evaluated the performance of the candidate processes utilizing single radars, multiple co-located radars and widely separated radars. The amplitude of the radar returns from frequency diverse sensors was exploited to aid in target/clutter discrimination as well as data association between sensors. Finally data from one or more passive sensors were fused with multiple radar data to further enhance the system performance. An extension to the 3D tracking research involved the development of processes for optimizing the location of the radar systems.

The objectives of the DataFusR II project were to:


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