Innovative Hypothesis Management

TIA Research will develop an approach for optimizing the performance of multi-hypothesis trackers. The work performed will consider primarily mechanisms for improving the association algorithm that is a fundamental element of the fusion process. In the first phase of this research, the proposed technique will be demonstrated and evaluated through its application to the fusion of multi-source track data. A test bed will be developed to support this evaluation, and a series of analyses will be performed to measure the improved performance versus more traditional techniques. The results of this research will be quantifiable performance measures demonstrating the utility of the approach and a set of prototypical software components suitable for extension and maturation under Phase II research.