Unified Bayesian Tracking of Threat Clouds

RVs are obscured by clouds of countermeasure scatterers that are of interest only because a few of them might be RVs. These threat clouds can be so dense that, for computational reasons, it is impossible to track every scatterer. One must track the threat clouds as wholes, until additional information makes it feasible to detect and extract the RVs. Conventional cluster tracking techniques such as centroid tracking will experience difficulty because threat clouds change dynamically in number, shape, and internal density. Scientific Systems Company, Inc. (SSCI) of Woburn MA and its subcontractor, Lockheed Martin Tactical Systems (LMTS) of Eagan MN, propose an innovative approach to multi-radar midcourse data fusion and cluster tracking that, we believe, is capable of addressing these challenges. Our "probability hypothesis density (PHD) filter" approach first attempts to detect and track only the OVER-ALL BULK BEHAVIOR---the physical distribution--of a formation that is obscured by clutter and missed detections. Only if the quantity and quality of data permits, does the filter then attempt to extract and track individual targets in the formation. The project team includes Dr. Ronald Mahler of Lockheed Martin. Lockheed Martin will provide both technical and commercialization support in the application of multitarget tracking and data fusion.