Research on Using a Situational Evaluation System to Assist in the Acquisition or Re-purposing of Instructional Technology

Not Available Scientific Systems Co., Inc. (SSCI) and its subcontractor Lockheed Martin Tactical Defense Systems (LMTDS) propose a direct, systematic, and principled generalization of Bayesian filtering and estimation theory to the problem of multisource, multitarget, multi-evidence unified joint detection, tracking, and target I.D. recursive Bayesian nonlinear filtering and estimation is the most accepted standard for developing algorithms that are optimal, theoretically defensible, and practical. However, the success of any optimal Bayes technique hinges on a number of subtle issues and in recent years shortcoming in conventional Bayesian thinking have become evident. In response to these, the SSCI-LMTDS approach is capable of addressing: (1) the ambiguity that is present in real-world ATR problems involving sources such as SAR, HRR, and datalink messages; (2) the real-time computational issues in single-target filtering, especially in low-SNR regimes; and (3) the unique conceptual and computational difficulties associated with optimal multitarget filtering. This approach finds its basis in finite set statistics (FISST_ a methodology developed at LMTDS. The project team includes Dr. Ronald Mahler of Lockheed Martin TDS the originator of FISST. Lockheed Martin will provide technical and commercialization support in the application of these technologies.