Advanced Data Fusion Using Bayesian Track-Before-Detect

We are proposing adding several enhancements to this new version of the Multi-Frame Probabilistic Multi-Hypothesis Tracker (MF-PMHT) in order to ready it for testing in the Ballistic Missile Defense (BMD) Benchmark and the X-Lab. During this effort a working prototype will be developed that will include multiple models, track management (initiation, deletion, splitting, and merging), covariance consistency, single track picture across platforms (SIAP), discrimination, bias estimation, launch point estimation, impact point prediction, and track-to-track fusion. The track-to-track fusion will be necessary for cases when measurement fusion is not available or feasible given the communication constraints. In addition, work will be done with the MF-PMHT in the SkyLOS sensor simulation both inside and outside of the BMD Benchmark. This work will prove the robust capability of this developing algorithm that is showing results that place it in a very competitive position both in terms of tracking performance and computational complexity.