Hybrid and Feature-Aided Multiple Hypothesis Correlation with Ambiguity Assessment

A critical need for BMD is innovative data fusion algorithms that enable the BMDS to meet performance standards by overcoming known limitations due to disparate, sparse, or degraded local (sensor) track or measurement data. A hybrid approach is sought that can correlate both measurement and track data. In addition, the fusion algorithm should be able to use feature, attribute and/or classification information in the data association process. The objective of this proposal is to develop a Hybrid Multiple Hypothesis Correlation (HMHC) system that (1) can appropriately fuse measurement and track data, (2) incorporate feature data to improve data association, (3) handle degraded or incomplete sensor data such as missing covariances, and (4) provide an assessment of the ambiguity in the reported track data and data association decisions to support improved processing of higher-level system functions. To ensure a direct transition path into the BMDS, Numerica proposes to develop this system as a significant capability extension to a Multiple Hypothesis Correlation (MHC) system for track-to-track fusion that Numerica recently developed for the Missile Defense National Team B. The proposed program topics have been identified by MDNTB as specific future needs for the C2BMC.