Multi-sensor Fusion for Integrating Tracking and Discrimination

Under this Phase II SBIR, the Decisive Analytics Corporation (DAC) team proposes two advanced capabilities that are not currently supported in the Ballistic Missile Defense System (BMDS). Employing ground-breaking techniques developed under Phase I of this effort for describing and solving the problems of tracking and discrimination within a single Bayesian network model, we propose the development of a high fidelity, multi sensor fusion framework for integrating tracking and discrimination. Additionally, we will formalize and automate elements of our innovative Multiple Hypothesis Bayesian Network framework for discrimination-aided track correlation, making it suitable for use in the operational BMDS. Both of these proposed approaches will build on the DAC team’s computational techniques for performing inference in large-scale network models. These technologies, combined with DAC’s experience in missile defense will result in algorithms suitable for simulated, end-to-end testing in Phase II, and integration and deployment to the BMDS via Phase III of this SBIR. The work outlined in this proposal has received a letter of endorsement from Raytheon, indicating the strong potential that exists for commercialization of this technology.