Optimizing Track-to-Track Data Fusion for Variable Cases

Many Intelligence, Surveillance, and Reconnaissance (ISR) systems involve combining track data from multiple sources, leading to an improved track picture. A multitude of track fusion algorithms exist, some of which require significant computational resources and have difficulty running in real-time when thousands of tracks must be maintained. Ideally, we would like to evaluate the utility of the various track fusion algorithms for a particular system. An analysis of different track fusion algorithms based on real data from the ISR system in which they will be deployed would yield the most accurate assessment of track fusion performance; however, this requires data from real experiments, which is often difficult to obtain in any quantity. An alternative is to test and evaluate algorithms within a simulated environment that can model to some degree of realism the conditions under which the system will operate (and can vary those conditions to conduct thorough trade studies). On this effort, Toyon will develop a T2T fusion test bed in order to analyze the performance of various T2T fusion algorithms under simulated scenarios of operation relevance to the Navy. The test bed will include an ISR simulation, T2T fusion application with multiple T2T association and fusion algorithms, and a performance analysis tool for calculating MOPs and MOEs.