Unified Robust-Bayes Multisource Counter-Terrorism Fusion

If the states and information sources for counter-terrorism problems were like thoseassociated with conventional sensor/target problems, then they could be solved usinga systematic and proven mathematical methodology---the Bayes filter. This filter providesan explicit methodology for modeling uncertainties, propagating these uncertainties throughtime, and extracting estimates of desired quantities (as well as measures of reliability of those estimates)that correctly reflect the influence of system uncertainties. The ambiguousness of humanintelligence information sources and of A PRIORI human cultural context would seem to automaticallypreclude the feasibility of the Bayes filter in counter-terrorism applications.Scientific Systems Company, Inc. (SSCI) and its subcontractor Lockheed Martin Tactical Systems (LMTS) believethat this may not be the case. Certain more conventional DoD problems---force structure analysis andsingle-target filtering using unconventional information (natural language, inference rules)---can beaddressed using Bayes filter methods, and these problems bear a family resemblance to counter-terrorism applications.Consequently, we propose the investigation of a novel Bayes-filter information-fusion approach to counter-terrorismapplications that both hedges against, and accounts for, inherent uncertainties.Specific Phase I tasks are: (1) develop a theoretical/mathematical/algorithmic foundation; (2) design high-leveltechniques for modeling states and measurements; (3) develop high-level designs for mathematical algorithms,including uncertainty assessment; (4) design potential test simulations; (5) develop implementation, simulation, and testplan; (6) develop a detailed plan for further analysis and implementation in a Phase II effort.The project team includes Dr. Ronald Mahler of Lockheed Martin. Lockheed Martin will provide both technicaland commercialization support in the application of counter-terrorism technologies.